A display device electromagnetic leakage signal detection method
By using broadband electromagnetic signal acquisition and multiple autocorrelation algorithms, combined with confidence level discrimination and adaptive threshold processing, the detection challenge of electromagnetic leakage signals in display devices under complex environments is solved, achieving high sensitivity and low false alarm rate in the entire detection process.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHONGBEI UNIV
- Filing Date
- 2026-04-10
- Publication Date
- 2026-07-21
AI Technical Summary
Existing electromagnetic leakage signal detection technologies for display devices cannot achieve high-performance detection in unknown models or complex electromagnetic environments. They suffer from high false alarm rates and low signal-to-noise ratios, making it difficult to adapt to the needs of all scenarios.
Broadband electromagnetic signal acquisition and preprocessing are employed, and core parameters are extracted by combining multiple autocorrelation algorithms. A confidence discrimination matrix is constructed, and signal enhancement is achieved through adaptive thresholding and multi-frame coherent accumulation. Finally, the detection results are output.
It achieves high-sensitivity detection without prior parameters, reduces false alarm rate, increases the probability of detecting weak leakage signals, and is compatible with the entire process detection of various display devices.
Smart Images
Figure CN121995121B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a method for detecting electromagnetic leakage signals in display devices, belonging to the field of electromagnetic detection technology. Background Technology
[0002] With the rapid development of information technology, display devices such as monitors, laptops, and all-in-one computers have been widely used in government, military, and financial sectors for processing classified information. During image data transmission and pixel driving, these devices generate high-frequency electromagnetic leakage carrying information about the displayed content. By receiving and analyzing these leakage signals, the displayed content can be reconstructed non-contactly, posing a serious threat to the security of classified information. Therefore, electromagnetic leakage signal detection technology for display devices is a core technology for electromagnetic environment monitoring in classified locations, information security risk assessment, and the construction of confidentiality protection systems, possessing significant research value and engineering significance.
[0003] Current electromagnetic leakage signal detection technology for display devices faces numerous technical bottlenecks, making it difficult to meet the high-performance detection requirements of complex electromagnetic environments and unknown display devices. Firstly, most existing leakage signal detection methods rely on prior parameters of the display device (such as resolution, pixel clock frequency, horizontal and vertical synchronization timing), requiring advance acquisition of device parameters to complete signal matching and detection. This makes blind detection impossible for display devices of unknown models or parameters, severely limiting applicable scenarios and failing to meet the needs of comprehensive electromagnetic environment monitoring in classified locations. Secondly, complex electromagnetic environments contain a large amount of environmental electromagnetic interference, co-band interference, and false synchronization signals. Existing detection methods do not fully utilize the inherent pixel amplitude and phase timing of leakage signals from display devices. First, existing technologies are characterized by their inability to accurately distinguish between valid leakage signals and various types of interference, resulting in a high false alarm rate and insufficient reliability of detection results. Second, in long-distance or high-interference scenarios, leakage signals attenuate severely, resulting in an extremely low signal-to-noise ratio. Weak leakage signals are easily submerged by noise. Existing multi-frame accumulation methods do not differentiate between valid signal features and interference features, making it impossible to achieve targeted enhancement of valid signals. The low detection probability of weak leakage signals has become a long-standing pain point in the industry. Third, existing technologies are mostly focused on optimizing a single detection step, without forming a complete detection solution from signal acquisition, initial detection, parameter extraction, fine discrimination, signal enhancement to result output. This makes engineering implementation difficult and makes it hard to adapt to the detection needs of various display devices.
[0004] In summary, existing technologies lack a method for detecting electromagnetic leakage signals from display devices throughout the entire process that requires no prior parameters, has a low false alarm rate, high detection sensitivity, and is adaptable to all scenarios. Summary of the Invention
[0005] To address the technical problems existing in the prior art, this invention provides a method for detecting electromagnetic leakage signals in display devices, which can effectively solve the problem that weak leakage signals are submerged by noise and have a low detection probability in long-distance, high-interference scenarios.
[0006] To achieve the above objectives, the technical solution adopted by the present invention is a method for detecting electromagnetic leakage signals in a display device, comprising the following steps: S1. Broadband electromagnetic signal acquisition and preprocessing: Broadband receiving equipment is used to acquire spatial electromagnetic signals of the target monitoring area. The spatial electromagnetic signal is then subjected to bandpass filtering, low-noise amplification, and quadrature downconversion processing in sequence to obtain a zero-IF baseband complex signal. Then, according to the sampling frequency that satisfies the Nyquist sampling theorem... Discrete sampling yields a discrete baseband complex signal sequence. ,in N is the total number of sampling points; S2. Employing multiple autocorrelation leakage signal detection and core parameter extraction for discrete baseband complex signal sequences. ,use Calculation of multiple autocorrelation algorithm The first-order multiple autocorrelation value, and then the... Peak detection is performed using multiple autocorrelation values to obtain the pixel clock frequency. Horizontal synchronization frequency Field synchronization frequency ; S3. Construct a confidence discrimination matrix based on pixel amplitude and phase timing features to discriminate the confidence of the effective leakage signal and accurately distinguish the effective leakage signal from various types of interference. S4. Then, weak signal enhancement is achieved through adaptive threshold feature selection and confidence-weighted multi-frame coherent accumulation, and finally the detection result is output.
[0007] Preferably, in step S2, the discrete baseband complex signal sequence is... ,use The algorithm for multiple autocorrelation of order 1 is as follows: , In the formula, where For discrete delay quantities, Let m be the order of autocorrelation calculation, m be the loop variable of the cumulative multiplication operator Π, and n be the discrete sampling point index of the signal sequence. This is the conjugate operation for complex signals; Discrete delay corresponding The first-order multiple autocorrelation value; For a periodic electromagnetic leakage signal carrying the displayed content, the discrete delay amount With signal period During matching, Significant peaks appear; while the multiple autocorrelation value of random noise increases with order. The increase in signal strength decreases exponentially, thus enabling initial detection of signals under low signal-to-noise ratio conditions.
[0008] Preferably, in step S2, the calculated... Perform peak detection: Set the peak detection threshold as follows Extract the discrete delay sequence corresponding to peak values exceeding the threshold, which is three times the global mean. ; Minimum discrete delay based on periodic constraints of leakage signals from display devices. Corresponding pixel clock cycle , obtain pixel clock frequency ; Integer multiples of discrete delay Corresponding row synchronization period To obtain the line synchronization frequency ; Integer multiples of discrete delay Corresponding field synchronization period The field synchronization frequency is obtained. .
[0009] Preferably, in step S3, when constructing the confidence discrimination matrix, the following steps are performed: S31, the discrete baseband complex signal sequence Synchronization cycle by field Divide into 1 frame to obtain Frame-by-frame continuous signal; each frame is synchronized by line period Divided into Rows, each row based on pixel clock cycles Divided into pixels, to obtain Pixel-level complex signal matrix of a frame , in: The frame number, Total number of frames The horizontal coordinates of the pixels; The vertical coordinates of the pixels; S32, Regarding the acquisition Pixel-level complex signal matrix of a frame Calculate the amplitude time series With phase timing sequence , , , in, For modulus operations on complex signals, Phase angle calculation for complex signals; S33, for pixels In all Amplitude timing sequence on frame Calculate the amplitude stability factor This characterizes the stability of the amplitude between frames: , In the formula, The mean of the pixel amplitude time series. The standard deviation of the pixel amplitude time series. To prevent extremely small positive numbers with a denominator of 0, a fixed value is used. ; The range of values is ; S34, for pixels In all On-frame phase timing sequence Calculate the phase coherence factor Characterizes the degree of coherence of the inter-frame phase: , In the formula: The imaginary unit; The range of values is The phase of the effective leakage signal has strong coherence. The phase of random disturbances tends to be 1; while the phase of random disturbances is uniformly distributed. Approaching 0; S35. Construct a confidence discrimination matrix. Combining the amplitude stability factor and the phase coherence factor, construct a pixel-level effective leakage signal confidence discrimination matrix. Since this confidence is based on the statistical characteristics of all frames, the confidence is the same for the same pixel across all frames, denoted as . , , in , For amplitude weighting, This is the phase weight.
[0010] Preferably, in step S4, when enhancing weak signals, the following steps are performed: S41. Select the lowest 10% pixel region in the confidence discrimination matrix as the noise reference region, adjust it according to the estimated effective signal ratio, and calculate the mean of the built-in confidence of the noise reference region. with standard deviation Design an adaptive confidence threshold , In the formula, Confidence coefficient; S42, Pixel-level confidence for all frames With adaptive threshold Based on the baseline, a selection process is conducted to eliminate those with a confidence level lower than [a certain threshold]. The low-confidence interference features and false signal features are extracted, while the high-confidence valid leakage signal features are retained to obtain the effective feature set. , S43. Confidence-weighted multi-frame coherent accumulation weak signal enhancement, targeting effective feature sets. The pixel signals within the matrix are used to achieve targeted enhancement of weak leakage signals using a confidence-weighted multi-frame coherent accumulation algorithm, resulting in an enhanced pixel-level complex signal matrix. : , In the formula, To prevent extremely small positive numbers with a denominator of 0, a fixed value is used. .
[0011] Preferably, in step S4, when outputting the result, the enhanced pixel-level complex signal matrix of the output is used as the basis. Complete the final confirmation and output of the detection results, and calculate the signal-to-noise ratio of the enhanced signal. : , In the formula, The average power of the effective pixel area; The average power of the noise reference region; Preset detection threshold ,when If a valid electromagnetic leakage signal from a display device is detected in the target area, it is determined that there is no valid leakage signal. If a valid leakage signal is determined to exist, the core parameters of the leakage signal are output synchronously, including pixel clock frequency, horizontal synchronization frequency, and vertical synchronization frequency, to complete the entire detection process.
[0012] Compared with the prior art, the present invention has the following technical effects: 1. A method for detecting leakage signals in display devices based on improved multiple autocorrelation is proposed. This method can achieve initial detection of leakage signals and extraction of core parameters such as horizontal and vertical synchronization and pixel clock without the need for any prior parameters of the display device. This solves the pain points of existing technologies that rely on prior parameters and have insufficient detection capabilities, and greatly expands the applicable scenarios of the detection method.
[0013] 2. Based on a multi-frame pixel-level complex signal matrix, inter-frame amplitude stability factors and phase coherence factors are designed respectively to construct a pixel-level effective leakage signal confidence discrimination matrix. By making full use of the inherent pixel timing stability characteristics of leakage signals of display devices, the effective signal can be accurately distinguished from various interference and false synchronization signals, thereby reducing the false alarm rate from the root and significantly improving the reliability of detection results.
[0014] 3. An inter-frame pixel feature selection algorithm based on noise level adaptive threshold was designed. By eliminating low-confidence interference features and retaining high-confidence effective features, and combining multi-frame coherent accumulation, weak leakage signals are enhanced, which significantly improves the detection probability of leakage signals in low signal-to-noise ratio environments and solves the industry pain point of weak leakage signals being difficult to detect.
[0015] 4. A complete detection solution has been developed, covering broadband signal acquisition, initial detection, feature extraction, fine discrimination, signal enhancement and result confirmation. It balances detection efficiency and performance, is compatible with electromagnetic leakage signal detection of various display devices, and can be widely used in scenarios such as electromagnetic environment monitoring in classified locations and information security risk assessment. It has strong practical engineering value. Attached Figure Description
[0016] Figure 1 This is a schematic diagram of the principle of the present invention.
[0017] Figure 2 This is a schematic diagram of the complex signal matrix in this invention.
[0018] Figure 3 This is a flowchart illustrating the implementation of the present invention. Detailed Implementation
[0019] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.
[0020] like Figures 1 to 3 As shown, a method for detecting electromagnetic leakage signals in a display device includes the following steps: Step 1: Broadband Electromagnetic Signal Acquisition and Preprocessing Spatial electromagnetic signals of the target monitoring area are acquired using broadband receiving equipment with an operating frequency range of 9kHz-6GHz. Broadband receiving equipment can employ a spectrum analyzer / software radio, and then sequentially perform bandpass filtering, 30-60dB low-noise amplification, and quadrature downconversion processing on the spatial electromagnetic signal to obtain a zero-IF baseband complex signal. Then, according to the sampling frequency that satisfies the Nyquist sampling theorem... Discrete sampling yields a discrete baseband complex signal sequence. ,in N is the total number of sampling points.
[0021] Step 2: Detection of leakage signals with multiple autocorrelation and extraction of core parameters
[0022] 1. For discrete baseband complex signal sequences ,use The algorithm for multiple autocorrelation of order 1 is as follows: , In the formula, where It is a discrete delay quantity, and its value range is... , The order of autocorrelation is taken as a positive integer, preferably within the range of 2-4; for low signal-to-noise ratio (SNR≤0dB), take... ; standard procedure m is the loop variable of the cumulative multiplication operator Π, and n is the discrete sampling point index of the signal sequence. This is the conjugate operation for complex signals; Discrete delay corresponding The first-order multiple autocorrelation value; in actual calculations, only when Time calculation Otherwise skip this. value.
[0023] For a periodic electromagnetic leakage signal carrying the displayed content, the discrete delay amount With signal period During matching, Significant peaks appear; while the multiple autocorrelation value of random noise increases with order. The increase in signal strength decreases exponentially, thus enabling initial detection of signals under low signal-to-noise ratio conditions.
[0024] 2. The calculated results Perform peak detection: Set the peak detection threshold as follows Extract the discrete delay sequence corresponding to peak values exceeding the threshold, which is three times the global mean. ; Minimum discrete delay based on periodic constraints of leakage signals from display devices. Corresponding pixel clock cycle , obtain pixel clock frequency ; Integer multiples of discrete delay Corresponding row synchronization period To obtain the line synchronization frequency ; Integer multiples of discrete delay Corresponding field synchronization period The field synchronization frequency is obtained. .
[0025] The third step involves constructing a confidence discrimination matrix based on pixel amplitude and phase temporal features to determine the confidence level of the valid leakage signal, accurately distinguishing the valid leakage signal from various types of interference. The specific steps are as follows: S31, the discrete baseband complex signal sequence Synchronization cycle by field Divide into 1 frame to obtain Frame-by-frame continuous signal; each frame is synchronized by line period Divided into Rows, each row based on pixel clock cycles Divided into pixels, to obtain Pixel-level complex signal matrix of a frame , in: The frame number, Total number of frames The horizontal coordinates of the pixels; The vertical coordinates of the pixels; S32, Regarding the acquisition Pixel-level complex signal matrix of a frame Calculate the amplitude time series With phase timing sequence , , , in, For modulus operations on complex signals, Phase angle calculation for complex signals; S33, for pixels In all Amplitude timing sequence on frame Calculate the amplitude stability factor This characterizes the stability of the amplitude between frames: , In the formula, The mean of the pixel amplitude time series. The standard deviation of the pixel amplitude time series. To prevent extremely small positive numbers with a denominator of 0, a fixed value is used. ; The range of values is The larger the value, the stronger the inter-frame stability of the pixel's amplitude, and the higher the probability of it being a valid leaked signal.
[0026] S34, for pixels In all On-frame phase timing sequence Calculate the phase coherence factor Characterizes the degree of coherence of the inter-frame phase: , In the formula: The imaginary unit; The range of values is The phase of the effective leakage signal has strong coherence. The phase of random disturbances tends to be 1; while the phase of random disturbances is uniformly distributed. Approaching 0; S35. Construct a confidence discrimination matrix. Combining the amplitude stability factor and the phase coherence factor, construct a pixel-level effective leakage signal confidence discrimination matrix. Since this confidence is based on the statistical characteristics of all frames, the confidence is the same for the same pixel across all frames, denoted as . , , in , For magnitude weighting, The phase weight, amplitude weight, and phase weight are adaptively adjusted according to the experiment or scenario. In scenarios with severe multipath effects and low signal-to-noise ratio, , ; In typical scenarios with minimal interference and primarily fluctuating amplitudes, take , , The range is The larger the value, the higher the confidence that the signal corresponding to that pixel is a valid leaked signal.
[0027] The fourth step involves enhancing weak signals through adaptive threshold feature selection and confidence-weighted multi-frame coherent accumulation, ultimately outputting the detection results. The specific steps are as follows: S41. Select the lowest 10% pixel region in the confidence discrimination matrix as the noise reference region, adjust it according to the estimated effective signal ratio, and calculate the mean of the built-in confidence of the noise reference region. with standard deviation Design an adaptive confidence threshold , In the formula, This is the confidence coefficient, with a default value of 3. It can be adjusted between 2 and 5 depending on the false alarm rate and detection sensitivity requirements. S42, Pixel-level confidence for all frames With adaptive threshold Based on the baseline, a selection process is conducted to eliminate those with a confidence level lower than [a certain threshold]. The low-confidence interference features and false signal features are extracted, while the high-confidence valid leakage signal features are retained to obtain the effective feature set. , S43. Confidence-weighted multi-frame coherent accumulation weak signal enhancement, targeting effective feature sets. The pixel signals within the matrix are used to achieve targeted enhancement of weak leakage signals using a confidence-weighted multi-frame coherent accumulation algorithm, resulting in an enhanced pixel-level complex signal matrix. : , In the formula, To prevent extremely small positive numbers with a denominator of 0, a fixed value is used. This weighted coherent accumulation effectively solves the problem of difficulty in detecting weak leakage signals in low signal-to-noise ratio environments.
[0028] S44. When outputting the results, based on the enhanced pixel-level complex signal matrix of the output. Complete the final confirmation and output of the detection results, and calculate the signal-to-noise ratio of the enhanced signal. : , In the formula, The average power of the effective pixel area; The average power of the noise reference region; Preset detection threshold , preset In practical applications, the threshold can be adjusted according to the specific scenario; when If a valid electromagnetic leakage signal from a display device is detected in the target area, it is determined that there is no valid leakage signal.
[0029] If a valid leakage signal is determined to exist, the core parameters of the leakage signal are output synchronously, including pixel clock frequency, horizontal synchronization frequency, and vertical synchronization frequency, to complete the entire detection process.
[0030] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included within the scope of the present invention.
Claims
1. A method for detecting electromagnetic leakage signals in a display device, characterized in that, Includes the following steps, S1. Broadband electromagnetic signal acquisition and preprocessing: Broadband receiving equipment is used to acquire spatial electromagnetic signals of the target monitoring area. The spatial electromagnetic signal is then subjected to bandpass filtering, low-noise amplification, and quadrature downconversion processing in sequence to obtain a zero-IF baseband complex signal. Then, according to the sampling frequency that satisfies the Nyquist sampling theorem... Discrete sampling yields a discrete baseband complex signal sequence. ,in N is the total number of sampling points; S2. Employing multiple autocorrelation leakage signal detection and core parameter extraction for discrete baseband complex signal sequences. ,use Calculation of multiple autocorrelation algorithm The first-order multiple autocorrelation value, and then the... Peak detection is performed using multiple autocorrelation values to obtain the pixel clock frequency. Horizontal synchronization frequency Field synchronization frequency ; S3. Construct a confidence discrimination matrix based on pixel amplitude and phase timing features to discriminate the confidence of the effective leakage signal and accurately distinguish the effective leakage signal from various types of interference. S4. Then, weak signal enhancement is achieved through adaptive threshold feature selection and confidence-weighted multi-frame coherent accumulation, and finally the detection result is output. In step S2, the discrete baseband complex signal sequence is... ,use The algorithm for multiple autocorrelation of order 1 is as follows: ， In the formula, where For discrete delay quantities, Let m be the order of autocorrelation calculation, m be the loop variable of the cumulative multiplication operator Π, and n be the discrete sampling point index of the signal sequence. This is the conjugate operation for complex signals; Discrete delay corresponding The first-order multiple autocorrelation value; For a periodic electromagnetic leakage signal carrying the displayed content, the discrete delay amount With signal period During matching, Significant peaks appear; while the multiple autocorrelation value of random noise increases with order. The increase in [signal value] decreases exponentially, thus achieving initial detection of signals under low signal-to-noise ratio; in step S3, when constructing the confidence discrimination matrix, the following steps are followed: S31, the discrete baseband complex signal sequence Synchronization cycle by field Divide into 1 frame to obtain Frame-continuous signal; Each frame is synchronized line by line. Divided into Rows, each row based on pixel clock cycles Divided into pixels, to obtain Pixel-level complex signal matrix of a frame , in: The frame number, Total number of frames The horizontal coordinates of the pixels; The vertical coordinates of the pixels; S32, Regarding the acquisition Pixel-level complex signal matrix of a frame Calculate the amplitude time series With phase timing sequence , ， ， in, For modulus operations on complex signals, Phase angle calculation for complex signals; S33, for pixels In all Amplitude timing sequence on frame Calculate the amplitude stability factor This characterizes the stability of the amplitude between frames: , In the formula, The mean of the pixel amplitude time series. The standard deviation of the pixel amplitude time series. To prevent extremely small positive numbers with a denominator of 0, a fixed value is used. ; The range of values is ; S34, for pixels In all On-frame phase timing sequence Calculate the phase coherence factor Characterizes the degree of coherence of the inter-frame phase: , In the formula: The imaginary unit; The range of values is The phase of the effective leakage signal has strong coherence. The phase of random disturbances tends to be 1; while the phase of random disturbances is uniformly distributed. Approaching 0; S35. Construct a confidence discrimination matrix. Combining the amplitude stability factor and the phase coherence factor, construct a pixel-level effective leakage signal confidence discrimination matrix. Since this confidence is based on the statistical characteristics of all frames, the confidence is the same for the same pixel across all frames, denoted as . , ， in , For amplitude weighting, This is the phase weight.
2. The method for detecting electromagnetic leakage signals in a display device according to claim 1, characterized in that, In step S2, the calculated Perform peak detection: Set the peak detection threshold as follows Extract the discrete delay sequence corresponding to peak values exceeding the threshold, which is three times the global mean. ; Minimum discrete delay based on periodic constraints of leakage signals from display devices. Corresponding pixel clock cycle , obtain pixel clock frequency ; Integer multiples of discrete delay Corresponding row synchronization period To obtain the line synchronization frequency ; Integer multiples of discrete delay Corresponding field synchronization period The field synchronization frequency is obtained. .
3. The method for detecting electromagnetic leakage signals in a display device according to claim 1, characterized in that, In step S4, when enhancing the weak signal, the following steps are performed: S41. Select the lowest 10% pixel region in the confidence discrimination matrix as the noise reference region, adjust it according to the estimated effective signal ratio, and calculate the mean of the built-in confidence of the noise reference region. with standard deviation Design an adaptive confidence threshold , In the formula, Confidence coefficient; S42, Pixel-level confidence for all frames With adaptive threshold Based on the baseline, a selection process is conducted to eliminate those with a confidence level lower than [a certain threshold]. The low-confidence interference features and false signal features are extracted, while the high-confidence valid leakage signal features are retained to obtain the effective feature set. ， S43. Confidence-weighted multi-frame coherent accumulation weak signal enhancement, targeting effective feature sets. The pixel signals within the matrix are used to achieve targeted enhancement of weak leakage signals using a confidence-weighted multi-frame coherent accumulation algorithm, resulting in an enhanced pixel-level complex signal matrix. : ， In the formula, To prevent extremely small positive numbers with a denominator of 0, a fixed value is used. .
4. The method for detecting electromagnetic leakage signals in a display device according to claim 3, characterized in that, In step S4, when outputting the result, the enhanced pixel-level complex signal matrix of the output is used as the basis. Complete the final confirmation and output of the detection results, and calculate the signal-to-noise ratio of the enhanced signal. : , In the formula, The average power of the effective pixel area; The average power of the noise reference region; Preset detection threshold ,when If a valid electromagnetic leakage signal from a display device is detected in the target area, it is determined that there is no valid leakage signal. If a valid leakage signal is determined to exist, the core parameters of the leakage signal are output synchronously, including pixel clock frequency, horizontal synchronization frequency, and vertical synchronization frequency, to complete the entire detection process.