Quantum random encryption method and device for video image information

Abstract

The embodiment of the invention provides a quantum random encryption method and device for video image information, and the unpredictability of a random source is ensured through the quantum mechanics principle by innovatively designing a quantum true random number generation mechanism. A dynamic partitioning system based on image entropy is constructed, and a multi-level data protection mechanism is established in combination with quantum key management. A secret key state monitoring and updating strategy is introduced, and safe transmission of data is achieved through symmetric encryption. According to the method, user privacy is protected from the quantum secret communication level, and the defects of a traditional encryption technology in the aspects of randomness, secret key management, transmission safety and the like are effectively overcome.

Description

Technical Field

[0001] This application relates to the field of information security, specifically to a quantum random encryption method and apparatus for video image information. Background Technology

[0002] Existing video and image encryption methods have significant shortcomings. Traditional systems perform poorly in random number generation, relying on pseudo-random algorithms, which makes it difficult to guarantee cryptographic security.

[0003] Furthermore, existing technologies have security vulnerabilities in data segmentation and key management. Most systems lack dynamic segmentation mechanisms and key update strategies, increasing the risk of data leakage.

[0004] Existing systems have technical shortcomings in encrypted transmission. The lack of consideration for quantum security makes them ill-equipped to handle the threats posed by quantum computing, impacting encryption reliability. Addressing these issues is crucial for protecting user privacy. Summary of the Invention

[0005] To address the problems in the prior art, this application provides a quantum random encryption method and apparatus for video image information, which can solve the shortcomings of traditional encryption technologies in terms of randomness, key management, and transmission security.

[0006] To solve at least one of the above problems, this application provides the following technical solution: In a first aspect, this application provides a quantum random encryption method for video image information, comprising: Quantum true random numbers are generated by a quantum random number generator. The quantum random number generator is based on the uncertainty principle of quantum mechanics. It uses a superluminescent diode to generate spontaneously emitted photons. The spontaneously emitted photons are subjected to noise evaluation. The randomness is verified by autocorrelation analysis. The verified random numbers are then post-processed by a hash compression algorithm. The video image information is preprocessed by segmentation, the region complexity is calculated based on the image entropy value, the segment size is dynamically adjusted according to the region complexity, optical flow maps are constructed for adjacent frames to extract motion information, key frames are identified, and a data block priority sequence is generated. Encryption operations are performed according to the data block priority sequence. The quantum random number is used to generate a pre-master key. A quantum key is obtained from the quantum key management system. The pre-master key and the quantum key are fused to generate a master key. The usage status of the master key is monitored. When a key failure is detected, a key update is triggered. The master key is used to generate a symmetric working key. The symmetric working key is used to encrypt and transmit video image information.

[0007] Furthermore, it also includes: adjusting the driving current of the superluminescent diode to a preset operating range, exciting spontaneously emitting photons under a preset bias voltage, converting the spontaneously emitting photons into electrical signals through a photodetector, amplifying and filtering the electrical signals using a signal conditioning circuit, sampling the analog signal into a digital sequence based on an analog-to-digital converter, constructing a phase noise evaluation model to analyze the quantum fluctuation characteristics in the digital sequence, and generating quantum noise evaluation parameters; The autocorrelation coefficient of the digital sequence is calculated, a multidimensional phase space reconstruction is constructed based on the delayed embedding method, the reconstruction dimension and delay time parameters are calculated, the randomness characteristics of the digital sequence are evaluated using generalized mutual information entropy, the quantum noise evaluation parameters and the randomness characteristics are input into a hash compression network, and quantum true random numbers are generated through multiple rounds of iterative compression operations.

[0008] Furthermore, it also includes: dividing video image information into basic data blocks, calculating the gray-level histogram distribution of the basic data blocks, calculating the image entropy value based on the information entropy formula for the gray-level distribution, constructing a texture feature extraction model based on gradient operators, inputting the image entropy value and texture feature vector into a complexity evaluation network, performing nonlinear mapping of features through a multilayer perceptron, and generating a region complexity score. A quadtree structure is constructed for the basic data blocks. The regional complexity score is input into the block adjustment model. The data blocks are recursively split and merged based on the score threshold. The block boundaries are optimized using a boundary smoothing algorithm to generate an adaptive block scheme. The adaptive block scheme is then applied to video image information to complete the block preprocessing.

[0009] Furthermore, it also includes: performing pyramid layering processing on adjacent video frames, constructing corner response maps in each layer, calculating pixel displacement fields based on constant brightness constraints, using bidirectional consistency checks to screen reliable motion vectors, densely propagating the motion vectors to obtain optical flow fields, using optical flow fields to calculate inter-frame motion amplitude and direction, and constructing a spatiotemporal consistency constraint model to smooth motion information; Based on the motion information, the inter-frame difference is calculated, and the inter-frame difference is input into the keyframe detection network to extract image gradient features and motion features. The features are weighted and fused through an attention mechanism to identify scene switching points and areas of intense motion as keyframes. Data blocks are assigned priority weights according to the keyframe position information to generate a data block priority sequence.

[0010] Furthermore, it also includes: inputting the quantum true random number into the pre-master key generation module, using a block cipher algorithm to expand and transform the random number, constructing a key derivation function to calculate the pre-master key, sending a key acquisition request to the quantum key management system, receiving the quantum key issued by the quantum key management system, performing integrity verification and decryption on the quantum key, and generating quantum key plaintext; The pre-master key and the quantum key are input into the key fusion network in plaintext. The key is mixed at the bit level based on XOR operation. An intermediate key material is generated using a key expansion algorithm. The intermediate key material is processed by a key derivation function. A cryptographic hash function is applied to calculate the master key. The master key is then verified for integrity and used for subsequent encryption operations.

[0011] Furthermore, it also includes: constructing a key status monitoring module to record the usage duration and encrypted data volume of the master key, setting an invalidation threshold based on the key update strategy, marking the key as invalid when the usage duration exceeds a preset period or the encrypted data volume reaches the threshold, triggering the key update process, re-requesting the quantum key management system to allocate a quantum key, and performing key fusion to generate a new master key; The master key is input into the key derivation module, a symmetric working key is generated using a key expansion algorithm, the video image information is grouped, the encryption order is determined according to the data block priority sequence, the symmetric working key is used to perform encryption operations on the data blocks to generate ciphertext data blocks, and the ciphertext data blocks are encapsulated into encrypted data packets for transmission.

[0012] Furthermore, it also includes: inputting the master key into the key derivation module, performing multi-round key expansion operations based on the round function, using an S-box for nonlinear transformation, applying a linear transformation matrix to confuse the intermediate states, merging the round keys through an XOR operation to generate a symmetric working key, and allocating the symmetric working key to the encryption processing unit; The video image information is grouped and filled. An encryption scheduling queue is constructed according to the priority sequence of the data blocks. The data blocks are input into the encryption processing unit. The symmetric working key is used to perform addition chain operation on the data blocks. Bit-level obfuscation is achieved through diffusion transformation. Nonlinear transformation is performed based on substitution box. Column mixing operation is applied to enhance encryption strength and generate ciphertext data blocks.

[0013] Secondly, this application provides a quantum random encryption device for video image information, comprising: The random number generation module is used to generate quantum true random numbers through a quantum random number generator. The quantum random number generator is based on the uncertainty principle of quantum mechanics, uses a superluminescent diode to generate spontaneously emitted photons, performs noise evaluation on the spontaneously emitted photons, verifies randomness using an autocorrelation analysis method, and performs post-processing on the verified random numbers using a hash compression algorithm. The image frame processing module is used to perform block preprocessing on video image information, calculate the region complexity based on the image entropy value, dynamically adjust the block size according to the region complexity, construct optical flow maps for adjacent frames to extract motion information, identify key frames, and generate a data block priority sequence. The quantum encryption module is used to perform encryption operations according to the priority sequence of the data block. The quantum random number is used to generate a pre-master key. The quantum key is obtained from the quantum key management system. The pre-master key and the quantum key are fused to generate a master key. The usage status of the master key is monitored. When the key failure is detected, the key is updated. The master key is used to generate a symmetric working key. The symmetric working key is used to encrypt and transmit video image information.

[0014] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the quantum random encryption method for video image information.

[0015] Fourthly, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the quantum random encryption method for the video image information.

[0016] Fifthly, this application provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the quantum random encryption method for the video image information.

[0017] As described above, this application provides a quantum random encryption method and apparatus for video image information. It innovatively designs a quantum true random number generation mechanism, ensuring the unpredictability of the random source through quantum mechanics principles. A dynamic block system based on image entropy is constructed, combined with quantum key management, to establish a multi-layered data protection mechanism. A key state monitoring and update strategy is introduced, and secure data transmission is achieved through symmetric encryption. This method protects user privacy at the quantum level and effectively solves the shortcomings of traditional encryption technologies in terms of randomness, key management, and transmission security. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the quantum random encryption method for video image information in an embodiment of this application. Figure 2 This is a structural diagram of the quantum random encryption device for video image information in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the electronic device in the embodiments of this application.

[0020] Figure label: Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver storage unit 9144, antenna 9111, speaker 9131, microphone 9132. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] The acquisition, storage, use, and processing of data in this application comply with relevant laws and regulations.

[0023] To address the problems existing in current technologies, this application provides a quantum random encryption method and apparatus for video image information. It innovatively designs a quantum true random number generation mechanism, ensuring the unpredictability of the random source through quantum mechanics principles. A dynamic block system based on image entropy is constructed, combined with quantum key management, to establish a multi-layered data protection mechanism. A key state monitoring and update strategy is introduced, and secure data transmission is achieved through symmetric encryption. This method protects user privacy at the quantum level and effectively solves the shortcomings of traditional encryption technologies in terms of randomness, key management, and transmission security.

[0024] To address the shortcomings of traditional encryption technologies in terms of randomness, key management, and transmission security, this application provides an embodiment of a quantum random encryption method for video image information. See [link to embodiment]. Figure 1 The quantum random encryption method for the video image information specifically includes the following: Step S101: Generate quantum true random numbers using a quantum random number generator. The quantum random number generator is based on the uncertainty principle of quantum mechanics, uses a superluminescent diode to generate spontaneously emitted photons, performs noise assessment on the spontaneously emitted photons, verifies randomness using autocorrelation analysis, and performs post-processing on the verified random numbers using a hash compression algorithm. Optionally, this embodiment constructs a random number generation mechanism based on quantum physical characteristics. The superluminescent diode used operates in a reverse bias state with a bias voltage range of -3V to -5V and an operating temperature maintained at 23±0.1℃.

[0025] In this operating state, the recombination process of charge carriers in the depletion region generates spontaneously emitted photons, and the photon emission time interval is entirely determined by quantum uncertainty. Through a precision temperature control system and voltage regulation circuit, this embodiment ensures that the light-emitting diode always operates in the quantum noise-dominated region, suppressing the influence of classical noise such as stray capacitance and dark current.

[0026] In practical applications, it has been found that small fluctuations in the junction temperature of the device can cause changes in the dark current. Therefore, a temperature compensation network is integrated into the bias circuit, and a thermistor is used for real-time compensation.

[0027] For the detection of spontaneously emitted photons, this embodiment uses a photomultiplier tube with a quantum efficiency of 65% and an output current sensitivity of 120A / W.

[0028] Specifically, the weak current signal generated by photoelectric conversion is first converted to voltage by a transimpedance amplifier, and then the signal amplitude is amplified to a suitable level range by a two-stage low-noise amplifier circuit.

[0029] During actual debugging, it was found that radio frequency interference could couple into the amplifier circuit through the power line and signal line, causing measurement errors.

[0030] This embodiment effectively reduces the impact of external interference by adding an LC filter network to the signal path and shielding key signal lines. The analog-to-digital conversion uses a 16-bit successive approximation ADC with a sampling rate of 125MHz, meeting the sampling requirements of quantum fluctuation signals.

[0031] This embodiment establishes a signal quality evaluation formula in the quantum noise evaluation stage: Q_score = w1 SNR + w2AC + w3*H_comp, where SNR is the signal-to-noise ratio, AC is the autocorrelation coefficient, H_comp is the hash compression ratio, and w1 / w2 / w3 are dynamic weighting coefficients.

[0032] For photoelectric detection signals, the power spectral density curve is first extracted to analyze the distribution characteristics of quantum noise and classical noise. In the frequency domain, quantum noise exhibits flat white noise characteristics, while classical noise is concentrated in specific frequency bands. Based on this characteristic, this embodiment designs an adaptive filtering algorithm to achieve accurate extraction of quantum noise.

[0033] In the randomness verification process, this embodiment segments the original data sequence into segments, each segment being 1024 bits in length. The independence of the data sequence is evaluated by calculating the autocorrelation function at different delay times.

[0034] Under certain operating conditions, a weak correlation was observed between adjacent sampling points due to device parasitic effects.

[0035] This embodiment eliminates this correlation by adjusting the sampling timing parameters and introducing decorrelation transformation in data post-processing. The hash compression process uses an improved SHA-3 algorithm, and the compression ratio can be adjusted from 1:2 to 1:4 according to application requirements.

[0036] In encryption applications, the quantum random number generator in this embodiment is directly integrated into the video processing unit to provide random number seeds for real-time encryption.

[0037] Considering the continuous nature of video images, the rate at which random numbers are generated needs to match the bandwidth of the video image data stream.

[0038] This embodiment achieves on-demand generation and cache reuse of random numbers through a cache management mechanism, solving the problem of insufficient generation rate of traditional random numbers.

[0039] In actual operation, real-time monitoring of random number quality was also implemented. When an abnormality in randomness indicators is detected, the system will automatically adjust the working parameters or switch to a backup channel.

[0040] Extensive testing and verification have shown that the quantum random number generator in this embodiment exhibits excellent randomness across various statistical indicators. In video encryption applications, this true random source significantly enhances the security of the encryption system, effectively preventing attacks based on random number prediction. Particularly in high-security scenarios, the quantum random number generator in this embodiment demonstrates clear technical advantages, providing a reliable source of random numbers for video encryption systems.

[0041] Step S102: Perform block preprocessing on the video image information, calculate the region complexity based on the image entropy value, dynamically adjust the block size according to the region complexity, construct optical flow maps for adjacent frames to extract motion information, identify key frames, and generate a data block priority sequence. Optionally, this embodiment develops an adaptive image segmentation mechanism based on the actual characteristics of the video content.

[0042] When processing massive amounts of video and image information, it has been found that segmenting data of the same size often cannot adequately meet the needs of different scenarios. For example, in security surveillance videos, densely populated areas require more detailed segments to retain sufficient detail, while open background areas can be adequately segmented with larger blocks.

[0043] After repeated testing, a dynamic block partitioning strategy based on region complexity was finally adopted, which adaptively adjusts the block size by calculating image entropy and texture features.

[0044] In particular, special attention is paid to the handling of edge areas during the segmentation process, because traditional rule-based segmentation can easily produce obvious block effects at the edges of objects, affecting the subsequent processing results.

[0045] This embodiment innovatively proposes a complexity scoring formula that integrates multi-dimensional features: R_score = w1×H_img + w2×T_det + w3×M_vec, where H_img represents the image entropy value, reflecting the amount of regional information; T_det represents the texture detail, describing local structural features; M_vec represents the motion vector intensity, reflecting temporal correlation; and the weight coefficients w1, w2, and w3 are automatically adjusted according to the video type.

[0046] In applications such as telemedicine video, the weights of entropy and texture features are increased to better preserve diagnostic image details. For fast-moving scenarios like sports broadcasts, the weight of motion features is increased.

[0047] In actual operation, an improved quadtree structure was used to adjust the block size. However, unlike traditional methods, this embodiment introduces spatial position constraints when determining the split, avoiding the generation of too many fragmented small blocks. Especially when processing drone aerial video, the boundaries of ground features are often irregularly distributed. If quadtree decomposition is used rigidly, it can easily lead to a large number of redundant small blocks in the boundary areas.

[0048] To address this issue, an irregular block segmentation algorithm based on edge manifolds was developed, which significantly reduces the number of blocks while ensuring fine segmentation of edge regions. During processing, it was also found that image noise interferes with the accuracy of complexity assessment. Therefore, an adaptive noise reduction mechanism was introduced before block segmentation, which preserves effective texture information while suppressing spurious complexity caused by noise.

[0049] Extracting optical flow information is a technical challenge. Initially, the classic Lucas-Kanade algorithm was used, but it often failed when dealing with large-scale displacements. After several improvements, a dense optical flow algorithm with a multi-scale pyramid structure was finally adopted to iteratively estimate motion information at different scales.

[0050] It is worth mentioning that optical flow estimation is prone to errors under low illumination conditions. This embodiment introduces a brightness adaptive mechanism to dynamically adjust the matching parameters according to local brightness conditions, which greatly improves the reliability of optical flow estimation.

[0051] In practical applications, optical flow computing adopts a block-parallel strategy, with each processing unit responsible for motion estimation of a local region, which improves processing efficiency and ensures real-time requirements.

[0052] In this embodiment, keyframe recognition employs a multi-feature fusion scheme. Relying solely on motion features can easily miss scene transitions where content changes significantly but motion is subtle. Therefore, a multimodal recognition framework is constructed, combining motion intensity, structural similarity, and depth features for comprehensive judgment.

[0053] For example, changes in the subtitle area are a crucial identification criterion in live news videos. Furthermore, scene-adaptive discrimination thresholds have been established for different types of video content, improving the accuracy of keyframe detection. Upon detecting a keyframe, a new round of segmentation optimization is triggered, ensuring that the segmentation strategy can be adjusted promptly when the scene changes.

[0054] The final generated data block priority sequence fully considers the needs of practical applications. For example: In digital rights management (DRM) scenarios, data blocks containing watermark information are given higher priority; in remote teaching videos, data blocks in the whiteboard area receive increased priority; and in medical image transmission, data blocks in the lesion area receive the highest priority. This differentiated processing strategy based on application scenarios ensures that critical information is fully protected during subsequent encrypted transmission.

[0055] The priority calculation process also takes into account temporal continuity to avoid drastic fluctuations in priority between adjacent frames, thus improving the smoothness of the visual experience.

[0056] Step S103: Perform encryption operation according to the data block priority sequence. The quantum random number is used to generate a pre-master key. Obtain the quantum key from the quantum key management system. Merge the pre-master key and the quantum key to generate a master key. Monitor the usage status of the master key. When a key failure is detected, trigger a key update. The master key generates a symmetric working key through calculation. Use the symmetric working key to encrypt and transmit video image information.

[0057] Optionally, this embodiment addresses the issues of key security and encryption efficiency during video encryption by designing a key generation scheme based on a quantum-classical hybrid approach.

[0058] The process first constructs a pre-master key using quantum random numbers, then obtains the quantum key from a quantum key management system, and finally generates the final master key through a special fusion algorithm. In practice, it was found that directly using quantum random numbers as the encryption key presents difficulties in distribution and management, while purely classical keys cannot guarantee sufficient security strength. Therefore, this hybrid approach ensures both the quantum randomness of the key and solves the practicality problem.

[0059] This embodiment designs a key strength evaluation formula: K_strength = α×Q_entropy + β×C_complexity + γ×F_freshness, where Q_entropy represents the quantum randomness strength, C_complexity represents the key complexity, F_freshness represents the key update timeliness, and α, β, and γ are dynamically adjusted weight coefficients.

[0060] The pre-master key is generated using a grouping process, which groups quantum random numbers into fixed-length groups, and each group of data is enhanced with features through a nonlinear transformation network.

[0061] In high-security scenarios such as encrypted telemedicine videos, the number of transformation network layers and complexity are increased. The transformation process also incorporates timestamp information to ensure that the pre-master keys generated at different times are sufficiently different.

[0062] In actual operation, it was found that the continuously generated pre-master keys may have a weak correlation. To address this issue, a decorrelation processing module was designed to break the possible correlation through operations such as XOR perturbation and bit rearrangement.

[0063] Acquiring quantum keys involves interaction with a quantum key management system. Each time a key is requested, authentication and authorization verification are required before a request packet containing encryption scenario information is sent to the management system.

[0064] The quantum key management system allocates appropriate quantum key resources based on the requester's security level and application scenario. After the acquired quantum key undergoes integrity verification, it must be decrypted to obtain usable plaintext.

[0065] In scenarios such as encryption of financial video and image information, more frequent key updates are often required. In such cases, a prefetching mechanism is established to cache a certain amount of quantum keys in advance.

[0066] The generation of the master key is a crucial step, requiring deep fusion of the pre-master key and the quantum key. The fusion process first involves bit-level mixing operations, and then uses a multi-round key expansion algorithm to generate intermediate key material.

[0067] Considering the varying security requirements of different application scenarios, the number of rounds in the extended algorithm can be flexibly adjusted. Next, a key derivation function is used to process the intermediate key material. This function incorporates application scenario characteristics, making the generated master key scenario-adaptable. Finally, the final master key is calculated using a cryptographic hash function and its integrity is verified.

[0068] During use, the master key's status is constantly monitored. The monitoring module records key metrics such as the duration of master key usage and the amount of encrypted data. When these metrics reach preset thresholds, a key update process is triggered. The update process re-executes the entire key generation process to ensure that key security does not decrease over time.

[0069] Especially in scenarios with high real-time requirements, such as video conferencing, key updates must be seamless to avoid impacting business continuity. To address this, a double-buffering mechanism was specifically designed to begin preparing a new key just before the current key expires, ensuring a seamless update process.

[0070] The symmetric working key is generated using an improved key expansion algorithm, which inputs the master key into an expansion network for multiple rounds of transformation. The transformation process includes operations such as nonlinear substitution, linear obfuscation, and round key merging to generate a working key sequence of sufficient length.

[0071] In high-definition video image encryption scenarios involving large data volumes, the working key generation rate must meet the requirements of real-time encryption. This embodiment significantly improves the efficiency of key expansion through hardware acceleration and parallel processing technologies.

[0072] The final encryption process strictly follows the data block priority sequence, with higher-priority data blocks receiving processing resources first, ensuring that important content is encrypted and transmitted in a timely manner.

[0073] As described above, the quantum random encryption method for video image information provided in this application can ensure the unpredictability of the random source by innovatively designing a quantum true random number generation mechanism and applying quantum mechanical principles. It constructs a dynamic block system based on image entropy and combines it with quantum key management to establish a multi-layered data protection mechanism. A key state monitoring and update strategy is introduced, and secure data transmission is achieved through symmetric encryption. This method protects user privacy at the quantum level and effectively solves the shortcomings of traditional encryption technologies in terms of randomness, key management, and transmission security.

[0074] In one embodiment of the quantum random encryption method for video image information in this application, it may further include the following: Step S201: Adjust the driving current of the superluminescent diode to a preset operating range, and generate spontaneous emission photons under a preset bias voltage. Convert the spontaneous emission photons into an electrical signal through a photodetector, amplify and filter the electrical signal using a signal conditioning circuit, sample the analog signal into a digital sequence based on an analog-to-digital converter, construct a phase noise evaluation model to analyze the quantum fluctuation characteristics in the digital sequence, and generate quantum noise evaluation parameters. Step S202: Calculate the autocorrelation coefficient of the digital sequence, construct a multidimensional phase space reconstruction based on the delayed embedding method, calculate the reconstruction dimension and delay time parameters, evaluate the randomness characteristics of the digital sequence using generalized mutual information entropy, input the quantum noise evaluation parameters and the randomness characteristics into a hash compression network, and generate quantum true random numbers through multiple rounds of iterative compression operations.

[0075] Optionally, this embodiment explores a quantum random number generation scheme based on superluminescent diodes (SLEDs). In SLEDs, the electronic energy level transition process generates spontaneously emitted photons, and the generation of these photons is entirely governed by quantum mechanical laws.

[0076] In practice, it was found that the operating state of the device directly affects the quantum properties of photons, requiring precise control of the driving current and bias voltage. After repeated testing, the optimal operating range was determined: the driving current is maintained within the quantum confinement region, while the bias voltage needs to be compensated for in real time, taking into account the temperature coefficient.

[0077] During operation, it was found that fluctuations in ambient temperature caused device characteristic drift. Therefore, a temperature feedback control mechanism was introduced to maintain a stable operating point by fine-tuning the bias parameters. This embodiment designs a quantum characteristic evaluation formula: Q_value = α×(P_noise) + β×(T_jitter) + γ×(C_correlation), where P_noise represents the phase noise intensity, T_jitter represents the time jitter amplitude, C_correlation represents the crosstalk suppression degree, and α, β, and γ are weighting coefficients dynamically adjusted according to application requirements.

[0078] The photoelectric detection stage uses a high-sensitivity photomultiplier tube, but in practical applications, it encounters problems such as dark counting and crosstalk.

[0079] To address these issues, a multi-stage signal conditioning circuit was designed. The first stage employs a low-noise transimpedance amplifier to convert the weak photocurrent into a voltage signal; the second stage uses a bandpass filter to suppress low-frequency drift and high-frequency interference; and the third stage introduces adaptive gain control to dynamically adjust the amplification factor based on the signal amplitude. During analog-to-digital conversion, a high-precision ADC is used for sampling, with the sampling rate and bit depth carefully designed to ensure the complete preservation of quantum fluctuation characteristics.

[0080] The phase noise assessment model has undergone several improvements. Initially, the traditional power spectrum analysis method was used, but it was difficult to distinguish between quantum noise and classical noise.

[0081] Later, a multi-resolution analysis method based on wavelet transform was adopted to separate quantum fluctuation features at different scales. Particular attention was paid to the spectral distribution characteristics of phase noise during the evaluation process, because pure quantum noise should exhibit white noise characteristics.

[0082] The purity of quantum properties can be effectively assessed by calculating the noise power ratio of different frequency bands.

[0083] A multi-level verification method was employed for the randomness analysis of numerical sequences. First, the autocorrelation coefficient was calculated to test the independence of the sequences. During the calculation, it was found that the choice of sampling interval significantly affected the results; too small an interval could lead to correlations between adjacent sampling points, while too large an interval might lose important information. By studying the correlation characteristics of the sequences, the optimal sampling strategy was determined.

[0084] The application of delayed embedding is an innovative point. By reconstructing a one-dimensional time series into a multi-dimensional phase space, the dynamic characteristics of the series can be analyzed more comprehensively. The choice of reconstruction parameters is crucial; too low a reconstruction dimension will lose the complexity of the system, while too high a dimension will introduce redundant information.

[0085] The introduction of generalized mutual information entropy significantly improves the reliability of stochasticity assessment. Compared to traditional Shannon entropy, generalized mutual information entropy can capture nonlinear correlations in sequences. The assessment process is divided into multiple scales, examining both local short-range correlations and analyzing global long-range dependencies.

[0086] In practical applications, different types of correlation may exhibit different characteristics, therefore an adaptive evaluation criterion is adopted. The final hash compression stage employs an improved compression algorithm, which continuously enhances randomness through multiple rounds of iterative computation while maintaining quantum properties.

[0087] Special care is taken to avoid introducing deterministic patterns during the compression process. The parameters for each iteration are extracted from the results of the previous round, ensuring the unpredictability of the final random number.

[0088] Through this series of meticulous processes, this embodiment achieves stable generation of high-quality quantum random numbers. It exhibits excellent performance in practical applications, particularly in cryptographic systems requiring high-strength random sources, providing a reliable foundation for key generation. This random number generation scheme based on quantum properties fundamentally avoids the periodicity and predictability problems inherent in traditional pseudo-random numbers.

[0089] In one embodiment of the quantum random encryption method for video image information in this application, it may further include the following: Step S301: Divide the video image information into basic data blocks, calculate the gray-level histogram distribution of the basic data blocks, calculate the image entropy value based on the information entropy formula, construct a texture feature extraction model based on gradient operators, input the image entropy value and texture feature vector into the complexity evaluation network, perform nonlinear mapping on the features through a multilayer perceptron, and generate a region complexity score. Step S302: Construct a quadtree structure for the basic data block, input the region complexity score into the block adjustment model, recursively split and merge the data block based on the score threshold, optimize the block boundary using a boundary smoothing algorithm, generate an adaptive block scheme, and apply the adaptive block scheme to video image information to complete block preprocessing.

[0090] Optionally, this embodiment studies a video segmentation processing scheme based on content features. Video image information is first divided into basic data blocks. In practice, it has been found that the size of the basic blocks is crucial. Blocks that are too large will lose local details, while blocks that are too small will increase the computational burden.

[0091] After multiple tests, a suitable base block size was selected. The grayscale histogram distribution of each base block can reflect the brightness and darkness characteristics of the area. However, simple grayscale statistics are often not accurate enough, because the same grayscale distribution may correspond to different image content.

[0092] This embodiment introduces an adaptive quantization method, dynamically adjusting the histogram binning strategy based on image characteristics to perform more detailed segmentation of frequently occurring grayscale values. Complexity evaluation uses a weighted calculation formula: C_score = w1×H_entropy + w2×T_feature + w3×E_edge, where H_entropy is the region entropy, T_feature is the texture complexity, and E_edge is the edge intensity. The weighting coefficients are automatically adjusted according to the video type.

[0093] When processing detailed videos such as medical images, traditional grayscale entropy calculation methods are not accurate enough. This embodiment designs an improved entropy calculation method that considers not only pixel grayscale values ​​but also local contrast information. Especially when processing low-contrast areas, contrast enhancement preprocessing improves the accuracy of entropy calculation.

[0094] Texture feature extraction employs a multi-scale gradient operator to calculate image gradients in different directions and construct gradient direction histograms. Practice revealed that single-scale gradient features are easily affected by noise; therefore, a multi-scale fusion strategy is adopted, which preserves fine texture while also possessing good noise resistance.

[0095] The design of the complexity evaluation network required considerable effort. Initial attempts at simple linear mapping yielded unsatisfactory results. Switching to a multilayer perceptron, the non-linear activation function better characterized the relationships between features. The network's input layer receives image entropy values ​​and texture feature vectors, the intermediate layers incorporate multiple non-linear mapping units, and the output layer provides a region complexity score.

[0096] The training phase utilized a large number of typical video samples, covering various scene types. The network architecture underwent multiple optimizations, ultimately achieving a good balance between accuracy and computational efficiency.

[0097] The construction of a quadtree requires careful attention to boundary handling. Each node represents a data block, and nodes with a complexity score exceeding a threshold are split into four child nodes. However, simple splitting can lead to drastic changes in block size, impacting coding efficiency.

[0098] This embodiment introduces a smoothing constraint, requiring that the size difference between adjacent blocks cannot be too large. In practical applications, the temporal continuity of the video must also be considered; the block structure of adjacent frames cannot change too drastically, otherwise flickering effects will occur. By setting a temporal smoothing constraint, the stability of the block structure is ensured.

[0099] Boundary optimization is a rather tricky problem.

[0100] Traditional quadtree segmentation can produce stair-step artifacts at object edges, affecting visual quality. This embodiment develops a boundary smoothing algorithm that adjusts the segmentation shape along the object boundary by detecting the directionality of the edge. When processing moving objects, the motion trajectory of the object is predicted, and the segmentation strategy is adjusted in advance to avoid boundary jitter. This content-based adaptive segmentation scheme significantly reduces computational complexity while maintaining encoding quality.

[0101] The entire segmentation preprocessing scheme performs excellently in actual video processing. Especially when dealing with videos featuring complex and varied scenes, it can flexibly adjust the segmentation strategy based on content characteristics. In applications like video conferencing, the areas containing people are segmented more finely to ensure clear transmission of facial expressions; while larger blocks are used in the background areas to save computational resources. This differentiated processing strategy ensures the quality of important content while controlling the overall computational burden.

[0102] In one embodiment of the quantum random encryption method for video image information in this application, it may further include the following: Step S401: Perform pyramid layering processing on adjacent frames of the video, construct a corner response map in each layer, calculate the pixel displacement field based on constant brightness constraint, use bidirectional consistency check to screen reliable motion vectors, densely propagate the motion vectors to obtain optical flow field, use optical flow field to calculate the inter-frame motion amplitude and direction, and construct a spatiotemporal consistency constraint model to smooth the motion information. Step S402: Calculate the inter-frame difference degree based on the motion information, input the inter-frame difference degree into the keyframe detection network, extract image gradient features and motion features, perform weighted fusion of features through an attention mechanism, identify scene switching points and intense motion intervals as keyframes, assign priority weights to data blocks according to the keyframe position information, and generate a data block priority sequence.

[0103] Optionally, this embodiment addresses the challenges of occlusion and large displacement in video motion analysis by designing a multi-scale optical flow estimation scheme. Before processing begins, an image pyramid is constructed for each video frame, with the resolution of each layer decreasing sequentially. This facilitates the handling of large-scale motion. In practice, it was found that simple Gaussian pyramids easily lose detail information; therefore, a Laplacian pyramid structure is adopted, which preserves information at different scales while reducing feature loss. The number of pyramid layers is not fixed but dynamically adjusted according to the video resolution, typically between 3 and 5 layers.

[0104] This embodiment designs a motion reliability scoring formula: M_score = w1×B_cons + w2×F_cons + w3×T_smooth, where B_cons represents backward consistency, F_cons represents forward consistency, T_smooth represents temporal smoothness, and the weighting coefficients will be automatically adjusted according to the characteristics of the video.

[0105] The construction of the corner response map is a complex process. Initially, the Harris corner detector was attempted, but it performed poorly in weakly textured regions. A modified Shi-Tomasi algorithm was then used, with adaptive thresholding improving the reliability of corner detection. The corner detection parameters need to be adjusted at each pyramid level. Especially when processing low-light videos, it is often necessary to lower the corner response threshold, but this easily introduces noise. To address this issue, an adaptive thresholding mechanism based on local contrast was introduced, which maintains detection sensitivity while suppressing the generation of false corners.

[0106] The pixel displacement field is calculated using an improved optical flow estimation method. The traditional constant brightness constraint is often violated in real-world scenarios, such as when illumination changes or the reflectivity of objects changes.

[0107] This embodiment introduces an illumination variation compensation mechanism, which improves the robustness of optical flow estimation by estimating a local brightness variation model. During the calculation, an iterative solution strategy is employed, updating the displacement estimate in each iteration until convergence or the maximum number of iterations is reached. To accelerate computation, a prediction mechanism is also introduced, using the optical flow field of the previous frame to perform an initial estimate for the current frame.

[0108] Selecting motion vectors is a crucial step in ensuring the quality of the optical flow field. Bidirectional consistency checks can effectively identify occlusion areas and unreliable matching points.

[0109] However, in some cases, even vectors that pass the consistency check may be inaccurate. For example, in regions with repetitive textures, multiple seemingly reasonable matching results may appear. This embodiment analyzes local motion consistency and combines it with spatial regularization constraints to select the most reasonable motion vector. For the filtered-out regions, a dense propagation algorithm is used to supplement them, ensuring the integrity of the optical flow field.

[0110] Spatiotemporal smoothing of motion information requires a balance between preserving motion details and suppressing noise. This embodiment constructs an adaptive spatiotemporal filter whose filtering intensity is dynamically adjusted according to the intensity of the motion. In regions of intense motion, the smoothing intensity in the temporal direction is reduced to preserve the characteristics of rapid motion; in regions of gentle motion, the smoothing intensity is increased to suppress noise fluctuations. This adaptive smoothing strategy ensures both the accuracy of motion information and provides stable motion characteristics.

[0111] The keyframe detection network employs an attention-enhanced dual-stream architecture. One stream processes static information such as appearance features, including gradients and textures; the other stream processes dynamic information such as motion features, including optical flow and motion amplitude. Through the attention mechanism, the network automatically focuses on important feature regions; for example, in sports videos, it pays more attention to the positions of athletes; in surveillance videos, it focuses on areas where abnormal behavior occurs. During feature fusion, an adaptive weighting strategy is used, with weight coefficients dynamically adjusted according to the scene content.

[0112] The determination of data block priorities takes into full account the importance of the video content. Data blocks in keyframes usually have higher priorities, but the specific weight allocation also needs to consider the block's location and content characteristics.

[0113] For example, in video conferencing scenarios, data blocks representing facial features receive higher priority; in security monitoring, areas containing moving targets are processed first. This content-aware priority allocation mechanism ensures that important information is fully protected in subsequent processing. The resulting priority sequence guides subsequent encryption and transmission processes, guaranteeing the quality of video services.

[0114] In one embodiment of the quantum random encryption method for video image information in this application, it may further include the following: Step S501: Input the quantum true random number into the pre-master key generation module, use a block cipher algorithm to expand and transform the random number, construct a key derivation function to calculate the pre-master key, send a key acquisition request to the quantum key management system, receive the quantum key issued by the quantum key management system, perform integrity verification and decryption on the quantum key, and generate quantum key plaintext; Step S502: Input the pre-master key and the quantum key plaintext into the key fusion network, perform bit-level mixing of the key based on XOR operation, generate intermediate key material using a key expansion algorithm, process the intermediate key material through a key derivation function, calculate the master key using a cryptographic hash function, and use the master key for subsequent encryption operations after integrity verification.

[0115] Optionally, this embodiment designs a hybrid key generation scheme from the perspective of key security. The quantum true random number is first sent to the pre-master key generation module, and this process cannot be rushed.

[0116] In practice, it has been found that directly using raw quantum random numbers is susceptible to noise and requires carefully designed preprocessing. The block cipher algorithm employs an improved AES structure, dividing random numbers into fixed-length blocks, each of which undergoes multiple rounds of nonlinear transformation. Timestamp information is introduced into the transformation process to ensure that the pre-master keys generated at different times are sufficiently different.

[0117] This embodiment designs a key quality evaluation formula: K_quality = α×E_entropy + β×D_diffusion + γ×C_correlation, where E_entropy represents the entropy strength, D_diffusion represents the diffusion degree, C_correlation represents the crosstalk suppression degree, and the weight coefficients will be dynamically adjusted according to the application scenario.

[0118] The process of acquiring quantum keys requires special attention to security. Identity authentication and authorization verification must be performed before sending a request to the quantum key management system.

[0119] In addition to basic identity information, the request packet must also include key parameters such as the usage scenario and security level. These parameters affect the management system's key allocation strategy. In high-security scenarios such as financial video image encryption, more frequent key updates are often required.

[0120] Therefore, this embodiment is designed with a prefetching mechanism to cache a certain amount of quantum keys in advance to avoid the impact of key acquisition delay on business continuity.

[0121] The obtained quantum key cannot be used directly; it needs to undergo integrity verification and decryption. The verification process employs a multi-layered authentication mechanism, including message authentication code verification and sequence number checking.

[0122] It is particularly important to note that ciphertext corruption may occur during the decryption process. To address this issue, this embodiment incorporates an error detection and recovery mechanism. If an error is detected in the key data, a re-acquisition process will be immediately triggered to ensure the reliability of the key.

[0123] Key fusion is a technical challenge. Initial attempts were made using simple XOR operations, but it was found that this approach might weaken the randomness of the quantum key.

[0124] The improved fusion network employs a multi-level hybrid structure, first performing a bit-level XOR operation, and then shuffling the bit distribution through a nonlinear transformation. Special care is taken to avoid introducing statistical bias during processing, maintaining key uniformity at each step. The design of the expansion algorithm is also crucial, finding a balance between expansion factor and security.

[0125] The intermediate key material is generated using an iterative process. Each iteration introduces a new perturbation factor to enhance the unpredictability of the key. In applications such as medical image encryption, the perturbation factor incorporates scene characteristics, making the generated key more closely aligned with practical needs. The key derivation function design draws inspiration from the HKDF framework but incorporates targeted improvements. Especially when processing long video sequences, the derivation function must ensure sufficient independence between the keys used in different frames.

[0126] An improved hash algorithm was used in the final generation stage of the master key. This algorithm not only had to guarantee the one-way nature of the key, but also had to consider computational efficiency.

[0127] In real-time video encryption scenarios, key generation latency must be kept within acceptable limits. Integrity verification employs multi-faceted checks, including entropy testing and randomness checks. Any anomalies trigger a regeneration process to ensure that each master key meets stringent security requirements.

[0128] This embodiment effectively combines the advantages of quantum randomness and traditional cryptography through this multi-level key generation scheme.

[0129] This hybrid approach demonstrates superior performance in practical applications, particularly when processing high-value data, providing more reliable security. The key generation process is fully controllable, and the update mechanism is flexible, meeting the security needs of different scenarios. For example, in video conferencing systems, the key update strategy can be dynamically adjusted according to the meeting level, providing stronger protection for important meetings.

[0130] In one embodiment of the quantum random encryption method for video image information in this application, it may further include the following: Step S601: Construct a key status monitoring module to record the usage duration and encrypted data volume of the master key, set an invalidation threshold based on the key update strategy, mark the key as invalid when the usage duration exceeds the preset period or the encrypted data volume reaches the threshold, trigger the key update process, re-request the quantum key management system to allocate a quantum key, and perform key fusion to generate a new master key. Step S602: Input the master key into the key derivation module, generate a symmetric working key using the key expansion algorithm, group the video image information, determine the encryption order according to the data block priority sequence, perform encryption operation on the data blocks using the symmetric working key to generate ciphertext data blocks, and encapsulate the ciphertext data blocks into encrypted data packets for transmission.

[0131] Optionally, this embodiment focuses on key lifecycle management and data encryption processing.

[0132] First, a key status monitoring mechanism was established; however, this monitoring is not simply a matter of recording a few parameters. In practice, it was found that key security gradually decreases with usage time and the amount of encrypted data. For example, when processing high-definition video streams, the same key, if used for too long, may reveal certain statistical characteristics.

[0133] To address this issue, a dynamic evaluation system was designed. This embodiment proposes a key state evaluation formula: K_state = w1×T_usage + w2×D_volume + w3×S_entropy, where T_usage represents the usage duration, D_volume represents the amount of encrypted data, S_entropy represents the security entropy value, and the weighting coefficients are dynamically adjusted according to the actual application scenario.

[0134] Key update strategies must consider multiple factors. In critical scenarios, such as medical video consultations, updates may be triggered in advance based on business needs, even if the key has not reached its expiration threshold. The monitoring module tracks key usage in real time and issues early warnings upon detecting abnormal patterns.

[0135] Interestingly, when processing ultra-high-definition video, it was found that the amount of encrypted data grew particularly rapidly, and the originally designed threshold might not have been suitable. After improvement, an adaptive threshold adjustment mechanism was adopted, in which the threshold is automatically adjusted according to the characteristics of the data stream.

[0136] The key update process is also crucial. It's unacceptable to wait until the key is completely expired before starting the update, as this would disrupt business continuity. This embodiment employs a pre-update mechanism, initiating preparations when the key approaches its expiration threshold. The update process is divided into several stages: first, requesting a new quantum key from the quantum key management system while simultaneously preparing the pre-master key materials; then, performing key fusion computation.

[0137] It is particularly important to note that the update process must ensure a smooth transition and must not affect ongoing encryption tasks.

[0138] The efficiency of the working key generation process must be carefully considered. Previously, traditional key expansion algorithms did not perform ideally when processing large-scale video information.

[0139] The improved algorithm employs a pipelined structure, allowing encryption operations to begin simultaneously with the generation of the working key. The extended algorithm introduces a parallel processing mechanism, with multiple processing units working concurrently, significantly increasing key generation speed. However, it's important to note that parallel processing can introduce correlations between keys, requiring special mechanisms to ensure key independence.

[0140] There's a technique to data block encryption. Simply grouping data into fixed sizes might fragment related data, impacting compression efficiency. This embodiment determines group boundaries based on the video's content characteristics, preserving the data's natural structure as much as possible. The application of priority sequences is also crucial; higher-priority data blocks receive processing resources first, ensuring timely encryption of important content. In security monitoring scenarios, moving target areas typically have higher priority and require priority processing.

[0141] The encryption operation employs an improved block cipher algorithm. Traditional algorithms suffer from several problems when processing video information, such as significant block artifacts and error propagation.

[0142] To address these issues, this embodiment incorporates an adaptive processing mechanism into the algorithm. The encryption strength is dynamically adjusted based on the importance of the data, with more rounds of encryption transformation applied to critical data. Error recovery capability is also considered, as data loss may occur during video transmission.

[0143] The final data encapsulation is also very important. Encapsulating encrypted data blocks is not simply a matter of packaging them together; a balance between transmission efficiency and security must also be considered.

[0144] This embodiment employs a hierarchical encapsulation structure, with different protection strategies at each level. In applications such as video conferencing, metadata and actual content are encrypted separately, using protection mechanisms of varying strengths. This differentiated approach ensures both security and improved transmission efficiency.

[0145] The entire solution performs excellently in practical applications. It demonstrates a good balance between security and efficiency, particularly when processing large-scale video streams. Key lifecycle management is highly flexible and adaptable to different scenarios. For example, in live streaming applications, the key update strategy can be dynamically adjusted based on the number of viewers, ensuring security while avoiding unnecessary update overhead.

[0146] In one embodiment of the quantum random encryption method for video image information in this application, it may further include the following: Step S701: Input the master key into the key derivation module, perform multi-round key expansion operation based on the round function, use the S-box for nonlinear transformation, apply the linear transformation matrix to confuse the intermediate state, merge the round keys through XOR operation to generate the symmetric working key, and allocate the symmetric working key to the encryption processing unit. Step S702: Group and fill the video image information, construct an encryption scheduling queue according to the data block priority sequence, input the data block into the encryption processing unit, perform addition chain operation on the data block using the symmetric working key, achieve bit-level obfuscation through diffusion transformation, perform nonlinear transformation based on substitution box, apply column mixing operation to enhance encryption strength, and generate ciphertext data block.

[0147] Optionally, this embodiment develops an efficient processing scheme for key derivation and data encryption issues in the video image information encryption process. The master key first enters the key derivation module, where the processing is crucial. Practice has shown that simple linear expansion can easily lead to correlations between working keys, which reduces encryption strength. Therefore, an iterative structure based on round functions is adopted, with each round including two stages: nonlinear transformation and obfuscation. The nonlinear transformation uses a dynamic S-box, whose construction considers balance and nonlinearity to ensure the cryptographic strength of the key expansion. This embodiment designs a key quality scoring formula: K_score = w1×N_degree + w2×D_uniform + w3×A_resist, where N_degree represents the degree of nonlinearity, D_uniform represents the uniformity index, and A_resist represents the resistance to attacks. The weighting coefficients are dynamically adjusted according to actual application requirements.

[0148] When processing large datasets such as ultra-high-definition video images, the efficiency of key expansion becomes particularly critical. Initially, a serial processing approach was used, but it was quickly found to be insufficient for real-time requirements. An improved approach introduced a parallel computing architecture, decomposing the key expansion process into multiple independent tasks. The confusion stage utilizes linear transformations designed with sparse matrices, reducing computational burden while maintaining effective diffusion. The round key merging process was also optimized, improving processing efficiency through a pipeline mechanism.

[0149] After the working key is generated, its distribution needs to be considered. In a distributed video processing system, the key needs to be securely distributed to each encryption processing unit. This embodiment employs a threshold-based key distribution mechanism, dividing the working key into multiple shares. Only when a sufficient number of shares are collected can the complete key be reconstructed. This approach not only improves the security of key management but also supports flexible authorization control. When receiving the key, the processing unit performs authentication and integrity verification to prevent key tampering or replacement.

[0150] The first step in data encryption is to address the block padding problem. Video images are not always divisible by the block size, and the last block often requires padding. Traditional padding schemes may introduce additional data redundancy, affecting transmission efficiency. This embodiment designs an adaptive padding algorithm that selects an appropriate padding mode based on the content characteristics of the data block. For insensitive areas such as image edges, simple padding can be used; while for important content areas, a more complex padding scheme is used to protect data integrity.

[0151] Encryption scheduling is a complex optimization problem. Processing data blocks directly according to priority may lead to uneven load distribution among processing units. This embodiment constructs a dynamic scheduling queue that comprehensively considers factors such as data block priority, processing unit status, and network bandwidth. The scheduling algorithm monitors the load of each processing unit in real time and dynamically adjusts the task allocation strategy. In scenarios with high real-time requirements, such as video conferencing, latency sensitivity must also be considered, prioritizing the processing of critical data blocks.

[0152] The specific encryption operations employ multiple protection mechanisms. Addition chaining provides basic encryption strength, while diffusion transformation ensures that a change in a single bit affects multiple output bits. Nonlinear transformation uses an optimized substitution box network, improving resistance to differential analysis. Column mixing operations further enhance encryption strength, giving the ciphertext a strong avalanche effect. This multi-layered protection mechanism provides flexible encryption schemes for video image information with different security levels.

[0153] The encryption scheme in this embodiment performs excellently in practical applications. For example, in telemedicine scenarios, patient privacy data is reliably protected; in video surveillance systems, video image information in important areas is given stronger encryption. The entire encryption process balances security and efficiency, meeting the various needs of modern video applications. Especially when processing high-bitrate video streams, parallelization and optimized design ensure the real-time performance of encryption processing.

[0154] To address the shortcomings of traditional encryption technologies in terms of randomness, key management, and transmission security, this application provides an embodiment of a quantum random encryption device for video image information that implements all or part of the quantum random encryption method for the aforementioned video image information. See [link to embodiment]. Figure 2 The quantum random encryption device for the video image information specifically includes the following components: The random number generation module 10 is used to generate quantum true random numbers through a quantum random number generator. The quantum random number generator is based on the uncertainty principle of quantum mechanics, uses a superluminescent diode to generate spontaneously emitted photons, performs noise evaluation on the spontaneously emitted photons, verifies randomness using an autocorrelation analysis method, and performs post-processing on the verified random numbers using a hash compression algorithm. Image frame processing module 20 is used to perform block preprocessing on video image information, calculate regional complexity based on image entropy value, dynamically adjust block size according to the regional complexity, construct optical flow map to extract motion information for adjacent frames, identify key frames, and generate data block priority sequence. The quantum encryption module 30 is used to perform encryption operations according to the priority sequence of the data block. The quantum random number is used to generate a pre-master key. The quantum key is obtained from the quantum key management system. The pre-master key and the quantum key are fused to generate a master key. The usage status of the master key is monitored. When the key failure is detected, the key is updated. The master key is used to generate a symmetric working key through calculation. The symmetric working key is used to encrypt and transmit video image information.

[0155] As described above, the quantum random encryption device for video image information provided in this application can ensure the unpredictability of the random source through an innovatively designed quantum true random number generation mechanism based on quantum mechanics principles. It constructs a dynamic block system based on image entropy, combined with quantum key management, to establish a multi-layered data protection mechanism. A key state monitoring and update strategy is introduced, and secure data transmission is achieved through symmetric encryption. This method protects user privacy at the quantum level and effectively solves the shortcomings of traditional encryption technologies in terms of randomness, key management, and transmission security.

[0156] From a hardware perspective, in order to address the shortcomings of traditional encryption technologies in terms of randomness, key management, and transmission security, this application provides an embodiment of an electronic device for implementing all or part of the quantum random encryption method for the video image information. The electronic device specifically includes the following components: The system comprises a processor, memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to realize information transmission between the quantum random encryption device for video image information and core business systems, user terminals, and related databases and other related devices; the logic controller can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the logic controller can be implemented with reference to the embodiments of the quantum random encryption method for video image information and the embodiments of the quantum random encryption device for video image information in the present embodiment, the contents of which are incorporated herein, and repeated details will not be described again.

[0157] It is understood that the user terminal may include smartphones, tablet computers, network set-top boxes, portable computers, desktop computers, personal digital assistants (PDAs), in-vehicle devices, smart wearable devices, etc. Among these, the smart wearable devices may include smart glasses, smartwatches, smart bracelets, etc.

[0158] In practical applications, the quantum random encryption method for video image information can be partially executed on the electronic device side as described above, or all operations can be completed in the client device. The choice can be made based on the processing power of the client device and the limitations of the user's usage scenario. This application does not impose any limitations on this. If all operations are completed in the client device, the client device may further include a processor.

[0159] The aforementioned client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side; in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a distributed server structure.

[0160] Figure 3 This is a schematic block diagram illustrating the system configuration of the electronic device 9600 according to an embodiment of this application. Figure 3 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that... Figure 3 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0161] In one embodiment, the quantum random encryption method for video image information can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control: Step S101: Generate quantum true random numbers using a quantum random number generator. The quantum random number generator is based on the uncertainty principle of quantum mechanics, uses a superluminescent diode to generate spontaneously emitted photons, performs noise assessment on the spontaneously emitted photons, verifies randomness using autocorrelation analysis, and performs post-processing on the verified random numbers using a hash compression algorithm. Step S102: Perform block preprocessing on the video image information, calculate the region complexity based on the image entropy value, dynamically adjust the block size according to the region complexity, construct optical flow maps for adjacent frames to extract motion information, identify key frames, and generate a data block priority sequence. Step S103: Perform encryption operation according to the data block priority sequence. The quantum random number is used to generate a pre-master key. Obtain the quantum key from the quantum key management system. Merge the pre-master key and the quantum key to generate a master key. Monitor the usage status of the master key. When a key failure is detected, trigger a key update. The master key generates a symmetric working key through calculation. Use the symmetric working key to encrypt and transmit video image information.

[0162] As described above, the electronic device provided in this application innovatively designs a quantum true random number generation mechanism, ensuring the unpredictability of the random source through quantum mechanics principles. It constructs a dynamic block system based on image entropy, combined with quantum key management, to establish a multi-layered data protection mechanism. A key state monitoring and update strategy is introduced, and secure data transmission is achieved through symmetric encryption. This method protects user privacy at the quantum level and effectively solves the shortcomings of traditional encryption technologies in terms of randomness, key management, and transmission security.

[0163] In another embodiment, the quantum random encryption device for video image information can be configured separately from the central processing unit 9100. For example, the quantum random encryption device for video image information can be configured as a chip connected to the central processing unit 9100, and the quantum random encryption method function for video image information can be implemented through the control of the central processing unit.

[0164] like Figure 3 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily need to include these components. Figure 3 All components shown; in addition, the electronic device 9600 may also include Figure 3 For components not shown, please refer to existing technologies.

[0165] like Figure 3 As shown, the central processing unit 9100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device, which receives inputs and controls the operation of various components of the electronic device 9600.

[0166] The memory 9140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 9100 may execute the program stored in the memory 9140 to perform information storage or processing, etc.

[0167] Input unit 9120 provides input to central processing unit 9100. Input unit 9120 may be, for example, a keypad or touch input device. Power supply 9170 provides power to electronic device 9600. Display 9160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0168] The memory 9140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 via the central processing unit 9100.

[0169] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0170] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processing unit 9100 to provide input signals and receive output signals, which is the same as in a conventional mobile communication terminal.

[0171] Based on different communication technologies, multiple communication modules 9110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby realizing typical telecommunications functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 9130 is coupled to a central processing unit 9100, enabling on-device recording via the microphone 9132 and on-device playback of stored audio via the speaker 9131.

[0172] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the quantum random encryption method for video image information with the execution subject being a server or client in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the quantum random encryption method for video image information with the execution subject being a server or client in the above embodiments. For example, when the processor executes the computer program, it implements the following steps: Step S101: Generate quantum true random numbers using a quantum random number generator. The quantum random number generator is based on the uncertainty principle of quantum mechanics, uses a superluminescent diode to generate spontaneously emitted photons, performs noise assessment on the spontaneously emitted photons, verifies randomness using autocorrelation analysis, and performs post-processing on the verified random numbers using a hash compression algorithm. Step S102: Perform block preprocessing on the video image information, calculate the region complexity based on the image entropy value, dynamically adjust the block size according to the region complexity, construct optical flow maps for adjacent frames to extract motion information, identify key frames, and generate a data block priority sequence. Step S103: Perform encryption operation according to the data block priority sequence. The quantum random number is used to generate a pre-master key. Obtain the quantum key from the quantum key management system. Merge the pre-master key and the quantum key to generate a master key. Monitor the usage status of the master key. When a key failure is detected, trigger a key update. The master key generates a symmetric working key through calculation. Use the symmetric working key to encrypt and transmit video image information.

[0173] As described above, the computer-readable storage medium provided in this application innovatively designs a quantum true random number generation mechanism, ensuring the unpredictability of the random source through quantum mechanics principles. It constructs a dynamic block system based on image entropy, combined with quantum key management, to establish a multi-layered data protection mechanism. A key state monitoring and update strategy is introduced, and secure data transmission is achieved through symmetric encryption. This method protects user privacy at the quantum level and effectively solves the shortcomings of traditional encryption technologies in terms of randomness, key management, and transmission security.

[0174] Embodiments of this application also provide a computer program product capable of implementing all steps of the quantum random encryption method for video image information where the execution subject is a server or client in the above embodiments. When executed by a processor, this computer program / instruction implements the steps of the quantum random encryption method for video image information. For example, the computer program / instruction implements the following steps: Step S101: Generate quantum true random numbers using a quantum random number generator. The quantum random number generator is based on the uncertainty principle of quantum mechanics, uses a superluminescent diode to generate spontaneously emitted photons, performs noise assessment on the spontaneously emitted photons, verifies randomness using autocorrelation analysis, and performs post-processing on the verified random numbers using a hash compression algorithm. Step S102: Perform block preprocessing on the video image information, calculate the region complexity based on the image entropy value, dynamically adjust the block size according to the region complexity, construct optical flow maps for adjacent frames to extract motion information, identify key frames, and generate a data block priority sequence. Step S103: Perform encryption operation according to the data block priority sequence. The quantum random number is used to generate a pre-master key. Obtain the quantum key from the quantum key management system. Merge the pre-master key and the quantum key to generate a master key. Monitor the usage status of the master key. When a key failure is detected, trigger a key update. The master key generates a symmetric working key through calculation. Use the symmetric working key to encrypt and transmit video image information.

[0175] As described above, the computer program product provided in this application innovatively designs a quantum true random number generation mechanism, ensuring the unpredictability of the random source through quantum mechanics principles. It constructs a dynamic block system based on image entropy, combined with quantum key management, to establish a multi-layered data protection mechanism. A key state monitoring and update strategy is introduced, and secure data transmission is achieved through symmetric encryption. This method protects user privacy at the quantum level and effectively solves the shortcomings of traditional encryption technologies in terms of randomness, key management, and transmission security.

[0176] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0177] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0178] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0179] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0180] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A quantum random encryption method for video image information, characterized in that, The method includes: Quantum true random numbers are generated by a quantum random number generator. The quantum random number generator is based on the uncertainty principle of quantum mechanics. It uses a superluminescent diode to generate spontaneously emitted photons. The spontaneously emitted photons are subjected to noise evaluation. The randomness is verified by autocorrelation analysis. The verified random numbers are then post-processed by a hash compression algorithm. The video image information is preprocessed by segmentation, the region complexity is calculated based on the image entropy value, the segment size is dynamically adjusted according to the region complexity, optical flow maps are constructed for adjacent frames to extract motion information, key frames are identified, and a data block priority sequence is generated. Encryption operations are performed according to the data block priority sequence. The quantum random number is used to generate a pre-master key. A quantum key is obtained from the quantum key management system. The pre-master key and the quantum key are fused to generate a master key. The usage status of the master key is monitored. When a key failure is detected, a key update is triggered. The master key is used to generate a symmetric working key. The symmetric working key is used to encrypt and transmit video image information.

2. The quantum random encryption method for video image information according to claim 1, characterized in that, The generation of quantum true random numbers via a quantum random number generator includes: The driving current of the superluminescent diode is adjusted to a preset operating range, and spontaneous emission photons are generated under a preset bias voltage. The spontaneous emission photons are converted into electrical signals by a photodetector, and the electrical signals are amplified and filtered by a signal conditioning circuit. The analog signal is sampled into a digital sequence based on an analog-to-digital converter, and a phase noise evaluation model is constructed to analyze the quantum fluctuation characteristics in the digital sequence and generate quantum noise evaluation parameters. The autocorrelation coefficient of the digital sequence is calculated, a multidimensional phase space reconstruction is constructed based on the delayed embedding method, the reconstruction dimension and delay time parameters are calculated, the randomness characteristics of the digital sequence are evaluated using generalized mutual information entropy, the quantum noise evaluation parameters and the randomness characteristics are input into a hash compression network, and quantum true random numbers are generated through multiple rounds of iterative compression operations.

3. The quantum random encryption method for video image information according to claim 1, characterized in that, The step of performing block preprocessing on video image information, calculating region complexity based on image entropy values, and dynamically adjusting the block size according to the region complexity includes: The video image information is divided into basic data blocks, the gray-level histogram distribution of the basic data blocks is calculated, the image entropy value is obtained by calculating the gray-level distribution based on the information entropy formula, a texture feature extraction model based on gradient operators is constructed, the image entropy value and texture feature vector are input into the complexity evaluation network, and the features are nonlinearly mapped through a multilayer perceptron to generate a region complexity score. A quadtree structure is constructed for the basic data blocks. The regional complexity score is input into the block adjustment model. The data blocks are recursively split and merged based on the score threshold. The block boundaries are optimized using a boundary smoothing algorithm to generate an adaptive block scheme. The adaptive block scheme is then applied to video image information to complete the block preprocessing.

4. The quantum random encryption method for video image information according to claim 1, characterized in that, The step of constructing optical flow maps of adjacent frames to extract motion information, identifying key frames, and generating a data block priority sequence includes: Pyramid layering is performed on adjacent frames of the video. A corner response map is constructed in each layer. The pixel displacement field is calculated based on the constant brightness constraint. A bidirectional consistency check is used to screen reliable motion vectors. The motion vectors are densely propagated to obtain an optical flow field. The optical flow field is used to calculate the motion amplitude and direction between frames. A spatiotemporal consistency constraint model is constructed to smooth the motion information. Based on the motion information, the inter-frame difference is calculated, and the inter-frame difference is input into the keyframe detection network to extract image gradient features and motion features. The features are weighted and fused through an attention mechanism to identify scene switching points and areas of intense motion as keyframes. Data blocks are assigned priority weights according to the keyframe position information to generate a data block priority sequence.

5. The quantum random encryption method for video image information according to claim 1, characterized in that, The step of performing encryption operations according to the data block priority sequence, using quantum random numbers to generate a pre-master key, obtaining a quantum key from a quantum key management system, and fusing the pre-master key with the quantum key to generate a master key includes: The quantum true random number is input into the pre-master key generation module, the random number is expanded and transformed using a block cipher algorithm, a key derivation function is constructed to calculate the pre-master key, a key acquisition request is sent to the quantum key management system, the quantum key issued by the quantum key management system is received, the quantum key is verified for integrity and decrypted, and the quantum key plaintext is generated. The pre-master key and the quantum key are input into the key fusion network in plaintext. The key is mixed at the bit level based on XOR operation. An intermediate key material is generated using a key expansion algorithm. The intermediate key material is processed by a key derivation function. A cryptographic hash function is applied to calculate the master key. The master key is then verified for integrity and used for subsequent encryption operations.

6. The quantum random encryption method for video image information according to claim 1, characterized in that, The monitoring of the master key's usage status, triggering a key update when key expiration is detected, and the master key generating a symmetric working key through calculation, using the symmetric working key to encrypt and transmit video image information, including: A key status monitoring module is constructed to record the usage duration and encrypted data volume of the master key. An invalidation threshold is set based on the key update strategy. When the usage duration exceeds the preset period or the encrypted data volume reaches the threshold, the key is marked as invalid, triggering the key update process. The quantum key management system is re-requested to allocate a quantum key, and key fusion is performed to generate a new master key. The master key is input into the key derivation module, a symmetric working key is generated using a key expansion algorithm, the video image information is grouped, the encryption order is determined according to the data block priority sequence, the symmetric working key is used to perform encryption operations on the data blocks to generate ciphertext data blocks, and the ciphertext data blocks are encapsulated into encrypted data packets for transmission.

7. The quantum random encryption method for video image information according to claim 6, characterized in that, The process of inputting the master key into the key derivation module, generating a symmetric working key using a key expansion algorithm, grouping the video image information, determining the encryption order according to the data block priority sequence, and performing encryption operations on the data blocks using the symmetric working key to generate ciphertext data blocks includes: The master key is input into the key derivation module, multi-round key expansion operation is performed based on the round function, nonlinear transformation is performed using the S-box, the intermediate state is confused by applying the linear transformation matrix, the round keys are merged by XOR operation to generate the symmetric working key, and the symmetric working key is allocated to the encryption processing unit. The video image information is grouped and filled. An encryption scheduling queue is constructed according to the priority sequence of the data blocks. The data blocks are input into the encryption processing unit. The symmetric working key is used to perform addition chain operation on the data blocks. Bit-level obfuscation is achieved through diffusion transformation. Nonlinear transformation is performed based on substitution box. Column mixing operation is applied to enhance encryption strength and generate ciphertext data blocks.

8. A quantum random encryption device for video image information, characterized in that, The device includes: The random number generation module is used to generate quantum true random numbers through a quantum random number generator. The quantum random number generator is based on the uncertainty principle of quantum mechanics, uses a superluminescent diode to generate spontaneously emitted photons, performs noise evaluation on the spontaneously emitted photons, verifies randomness using an autocorrelation analysis method, and performs post-processing on the verified random numbers using a hash compression algorithm. The image frame processing module is used to perform block preprocessing on video image information, calculate the region complexity based on the image entropy value, dynamically adjust the block size according to the region complexity, construct optical flow maps for adjacent frames to extract motion information, identify key frames, and generate a data block priority sequence. The quantum encryption module is used to perform encryption operations according to the priority sequence of the data block. The quantum random number is used to generate a pre-master key. The quantum key is obtained from the quantum key management system. The pre-master key and the quantum key are fused to generate a master key. The usage status of the master key is monitored. When the key failure is detected, the key is updated. The master key is used to generate a symmetric working key. The symmetric working key is used to encrypt and transmit video image information.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the quantum random encryption method for video image information as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the quantum random encryption method for video image information as described in any one of claims 1 to 7.

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