A method and device for preventing eavesdropping, a computer device and a storage medium

Abstract

The application relates to the technical field of mobile communication, and discloses an anti-interception method and device, computer equipment and a storage medium, the method comprising the following steps: when an anti-interception function is triggered, collecting audio parameters in a target space; the audio parameters comprise environmental audio information and human voice audio information; analyzing the audio parameters, and generating adaptive interference noise according to the analysis result; the interference noise is used for interfering with the monitoring of a monitoring device on the current target space. By collecting and analyzing the audio parameters in the target space, the acoustic characteristics of the current space can be accurately grasped, adaptive interference noise is accurately generated based on the acoustic characteristics, the monitoring of the monitoring device on the target space is efficiently interfered with, and the success rate and effectiveness of the anti-interception are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technology, and in particular to an anti-eavesdropping method, device, computer equipment and storage medium. Background Art

[0002] In today's highly information-sensitive social environment, the risks of surveillance are becoming increasingly severe, whether in the areas of personal privacy protection, commercial secrets, national security, etc.

[0003] As various types of monitoring devices continue to develop and become increasingly covert, traditional anti-surveillance methods often have limitations and are unable to cope with complex and changing monitoring scenarios. For example, some fixed-frequency jamming devices are less effective against intelligent, adaptive monitoring devices. They can easily cause unnecessary interference to normal communications and the environment, and cannot accurately target the actual audio conditions in the target space to effectively protect people from the threat of illegal monitoring in different scenarios.

[0004] The above information is presented as background information only to assist with an understanding of the present disclosure and is not a determination or admission that any of the above may be applicable as prior art with respect to the present disclosure. Summary of the Invention

[0005] The present invention provides an anti-eavesdropping method, device, computer equipment and storage medium to solve the problems existing in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An adaptive anti-eavesdropping method, comprising:

[0008] When the anti-monitoring function is triggered, the audio parameters in the target space are collected; the audio parameters include ambient audio information and human voice audio information;

[0009] The audio parameters are analyzed, and corresponding interference noise is generated according to the analysis results; the interference noise is used to interfere with the monitoring of the current target space by the monitoring device.

[0010] Optionally, the adaptive anti-eavesdropping method includes:

[0011] Collecting voice information in the target space and analyzing whether there are preset monitoring keywords;

[0012] When the preset monitoring keywords are present, the anti-monitoring function is automatically triggered.

[0013] Optionally, the adaptive anti-eavesdropping method further includes:

[0014] Use the camera to capture the image of the person in the target space in real time and compare it with the specific person image in the database;

[0015] When a matching specific person image is identified, the mouth movements of the corresponding specific person are identified to determine whether the specific person is speaking;

[0016] When it is recognized that the specific person is speaking, the anti-monitoring function is automatically triggered.

[0017] Optionally, the adaptive anti-eavesdropping method further includes:

[0018] Continuously monitor the sound signals in the target space, analyze the timbre of the sound signals, and compare them with the voice characteristics of specific people in the database;

[0019] When a matching specific person's voice characteristics are recognized, the anti-monitoring function is automatically triggered.

[0020] Optionally, analyzing the audio parameters includes:

[0021] The environmental audio information and human voice audio information are analyzed from the three dimensions of sound frequency, volume and intensity, and the energy proportion, loudness value and intensity value of the environmental audio and human voice audio in each frequency band are obtained respectively.

[0022] Optionally, generating an adaptive interference noise according to the analysis result includes:

[0023] Based on the analysis results, the energy proportion parameters, loudness parameters and intensity parameters of the interference noise in each frequency band are allocated to generate interference noise.

[0024] Optionally, generating an adaptive interference noise according to the analysis result further includes:

[0025] The interference noise is caused to randomly jump within a predetermined frequency band.

[0026] Optionally, causing the interference noise to randomly jump within a predetermined frequency band includes:

[0027] When the ambient audio and / or human voice audio are distributed in the low frequency band, causing the interference noise to randomly jump within the low frequency band;

[0028] When the ambient audio and / or human voice audio are distributed in the mid-frequency band, the interference noise is randomly jumped in the mid-frequency band and in the transition frequency bands between the mid-frequency band and the high frequency band and the low frequency band;

[0029] When the ambient audio and / or human voice audio are distributed in the high frequency band, the interference noise is caused to randomly jump within the high frequency band.

[0030] Optionally, analyzing the audio parameters and generating an adaptive interference noise according to the analysis result includes:

[0031] Identifying the human voice audio information, and when a special timbre exists, identifying a characteristic frequency band of the special timbre;

[0032] According to the characteristic frequency band of the special timbre, interference noise with an inverse spectrum is generated.

[0033] The present invention also provides an anti-eavesdropping device for implementing the adaptive anti-eavesdropping method as described in any one of the above items, comprising:

[0034] A signal acquisition unit, configured to collect audio parameters in the target space when the anti-monitoring function is triggered; the audio parameters include ambient audio information and human voice audio information;

[0035] The analysis and processing unit is used to analyze the audio parameters and generate corresponding interference noise according to the analysis results; the interference noise is used to interfere with the monitoring of the monitoring device in the current target space.

[0036] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the adaptive anti-eavesdropping method as described in any one of the above items when executing the computer program.

[0037] The present invention also provides a storage medium containing computer-executable instructions, wherein the computer-executable instructions are executed by a computer processor to implement the adaptive anti-eavesdropping method as described in any one of the above items.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention provides an anti-eavesdropping method, device, computer equipment, and storage medium. By collecting and analyzing audio parameters in a target space, the method can accurately grasp the acoustic characteristics of the current space and generate adaptive interference noise based on this precise analysis, thereby effectively interfering with the monitoring equipment's monitoring of the target space, greatly improving the success rate and effectiveness of anti-eavesdropping.

[0040] The present invention has other features and advantages that will be apparent from or will be described in detail in the accompanying drawings and the following detailed description incorporated herein, which together serve to explain certain principles of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 This is a flow chart of an anti-eavesdropping method provided in Example 1 of the present invention;

[0043] Figure 2 This is a flowchart of a triggering mechanism in an anti-eavesdropping method provided in Example 1 of the present invention;

[0044] Figure 3 This is another flow chart of a triggering mechanism in an anti-eavesdropping method provided in the first embodiment of the present invention;

[0045] Figure 4 This is another flow chart of a triggering mechanism in an anti-eavesdropping method provided in the first embodiment of the present invention;

[0046] Figure 5 This is a flowchart of step S2 in an anti-eavesdropping method provided in Example 1 of the present invention;

[0047] Figure 6 This is another flow chart of step S2 in the anti-eavesdropping method provided in the first embodiment of the present invention;

[0048] Figure 7 This is another flow chart of step S2 in the anti-eavesdropping method provided in the first embodiment of the present invention;

[0049] Figure 8 This is a structural block diagram of an anti-eavesdropping device provided in Example 1 of the present invention.

[0050] Reference numerals: 10, signal acquisition unit; 20, analysis and processing unit. DETAILED DESCRIPTION

[0051] In order to explain in detail the possible application scenarios, technical principles, specific solutions that can be implemented, and the purpose and effects of this application, the following is a detailed description of the specific embodiments listed in conjunction with the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of this application and are therefore only examples and are not intended to limit the scope of protection of this application.

[0052] References to "embodiments" herein mean that the specific features, structures, or characteristics described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the word "embodiment" in various places in the specification does not necessarily refer to the same embodiment, nor does it particularly limit its independence or relevance to other embodiments. In principle, in this application, as long as there are no technical contradictions or conflicts, the various technical features mentioned in the embodiments can be combined in any manner to form a corresponding implementable technical solution.

[0053] Unless otherwise defined, the technical terms used herein have the same meanings as those generally understood by those skilled in the art to which this application belongs; the use of relevant terms herein is only for describing specific embodiments and is not intended to limit this application.

[0054] In the description of this application, the term "and / or" is used to describe a logical relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A exists, B exists, and both A and B exist. In addition, the character " / " in this document generally indicates that the objects before and after are in a logical "or" relationship.

[0055] In this application, terms such as "first" and "second" are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual quantity, priority or sequence relationship between these entities or operations.

[0056] Without further limitations, in this application, the words "include", "comprise", "have" or other similar expressions used in the sentences are intended to cover non-exclusive inclusion. These expressions do not exclude the presence of additional elements in the process, method or product including the elements, so that the process, method or product including a series of elements may include not only those defined elements, but also other elements not explicitly listed, or elements inherent to such process, method or product.

[0057] Consistent with the understanding in the Examination Guidelines, in this application, expressions such as "greater than," "less than," and "exceed" are understood to exclude the number itself; expressions such as "above," "below," and "within" are understood to include the number itself. Furthermore, in the description of the embodiments of this application, "multiple" means more than two (including two), and similar expressions related to "multiple" are also understood in this manner, such as "multiple groups," "multiple times," etc., unless otherwise specifically defined.

[0058] In the description of the embodiments of the present application, the space-related expressions used, such as "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "vertical", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicate the orientation or position relationship based on the orientation or position relationship shown in the specific embodiments or drawings, and are only for the convenience of describing the specific embodiments of the present application or facilitating the reader's understanding, and do not indicate or imply that the device or component referred to must have a specific position, a specific orientation, or be constructed or operated in a specific orientation. Therefore, it should not be understood as a limitation on the embodiments of the present application.

[0059] Unless otherwise expressly specified or limited, in the description of the embodiments of the present application, the terms "installed", "connected", "connected", "fixed", "set", etc. used should be understood in a broad sense. For example, the "connection" can be a fixed connection, a detachable connection, or an integrated setting; it can be a mechanical connection, an electrical connection, or a communication connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. For those skilled in the art of the present application, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances. Example 1

[0060] Please refer to Figure 1 , an embodiment of the present invention provides an adaptive anti-eavesdropping method, comprising:

[0061] S1. When the anti-monitoring function is triggered, the audio parameters in the target space are collected.

[0062] The audio parameters include ambient audio information and human voice audio information.

[0063] S2. Analyze audio parameters and generate appropriate interference noise based on the analysis results.

[0064] The interference noise is used to interfere with the monitoring of the monitoring device in the current target space.

[0065] In this embodiment, when the anti-eavesdropping function is triggered, the audio collection device comprehensively collects audio parameters within the target space. These audio parameters include ambient audio information and human voice audio information. Ambient audio information includes background sounds such as the natural environment and equipment operation sounds within the target space, while human voice audio information focuses on the speech content of people within the space.

[0066] For example, in a conference room scenario, the ambient audio may include the sound of air conditioning, projector fans, etc., while the human voice audio is the discussion and speeches of the participants.

[0067] In practical applications, digital signal processing-based jammers can be used to generate interference noise. Based on the input audio parameters, these jammers can precisely generate interference noise with specific spectrum, amplitude, and phase characteristics, adapting to the target audio and achieving a highly effective jamming effect.

[0068] Please refer to Figure 2 In this embodiment, the adaptive anti-eavesdropping method further includes:

[0069] S011. Collect voice information in the target space and analyze whether there is a preset monitoring keyword;

[0070] S012. When a preset monitoring keyword exists, the anti-monitoring function is automatically triggered.

[0071] By setting up a keyword monitoring mechanism, voice information in the target space is continuously collected. Once the presence of preset monitoring keywords is detected, such as specific words involving commercial secrets, sensitive political topics, etc., the anti-monitoring function is automatically triggered, and the above-mentioned audio collection, analysis and interference noise generation and processing processes are fully activated to ensure the security of information exchange in the target space, effectively resist various types of monitoring threats, and maintain the confidentiality and security of information.

[0072] In actual application scenarios, in addition to the existing keyword monitoring mechanism, the monitoring of words related to confidential content can be further refined and strengthened.

[0073] For example, strict monitoring focuses on key sensitive words such as financial information (such as bank account balances, credit card security codes, and online banking login verification codes), personal identity information (such as ID numbers, passport numbers, home addresses, and Social Security numbers), trade secrets (such as undisclosed product development details, customer lists, marketing strategies, bidding information, and financial data), and state and government secrets (such as military deployment plans, intelligence sources and methods, and national strategic plans). Once relevant keywords are detected, the anti-surveillance function is automatically triggered, initiating audio collection and analysis, as well as interference noise generation and processing. This prevents serious consequences such as financial theft, identity theft, loss of business advantage, and threats to national security, ensuring the security and confidentiality of information exchange.

[0074] Please refer to Figure 3 In this embodiment, the adaptive anti-eavesdropping method further includes:

[0075] S021. Capture the image of the person in the target space in real time through the camera, and compare it with the specific person image in the database;

[0076] S022. When a matching specific person image is identified, identify the mouth movements of the corresponding specific person and determine whether the specific person is speaking;

[0077] S023. When a specific person is identified as speaking, the anti-monitoring function is automatically triggered.

[0078] In the aforementioned steps, a camera captures an image of a person and compares it with pre-stored images of specific people in a database to monitor that person. When a specific person appears in the target space, their mouth movements are tracked and analyzed. If they speak, the anti-eavesdropping function is automatically triggered, preventing their speech from being monitored, effectively preventing the leakage of confidential information.

[0079] Please refer to Figure 4 In this embodiment, the adaptive anti-eavesdropping method further includes:

[0080] S031. Continuously monitor the sound signals in the target space, perform timbre analysis on the sound signals, and compare them with the voice characteristics of specific people in the database;

[0081] S032. When a matching voice feature of a specific person is recognized, the anti-monitoring function is automatically triggered.

[0082] In this embodiment, an enhanced prevention mechanism is set up for specific people. The speech of specific people is monitored from the dimension of sound, and the anti-eavesdropping function is automatically activated when the specific person speaks, so as to improve the protection against leakage of confidential content.

[0083] Furthermore, in step S2, analyzing audio parameters includes:

[0084] The environmental audio information and human voice audio information are analyzed from the three dimensions of sound frequency, volume and intensity, and the energy proportion, loudness value and intensity value of the environmental audio and human voice audio in each frequency band are obtained respectively.

[0085] Please refer to Figure 5 Furthermore, in step S2, generating an adaptive interference noise according to the analysis result includes:

[0086] S21. Based on the analysis results, allocate energy proportion parameters, loudness parameters, and intensity parameters of the interference noise in each frequency band to generate interference noise.

[0087] In this embodiment, audio parameter analysis is performed using three key dimensions: frequency, volume, and intensity. In the frequency dimension, the energy contribution of the ambient and vocal audio in each frequency band, such as low, mid, and high, is accurately calculated. The volume dimension derives a loudness value to clarify the loudness of the sound. Furthermore, the intensity dimension determines an intensity value to reflect the energy level of the sound.

[0088] Based on these precise analysis results, the system constructs an adaptive interference noise model based on the energy percentage, loudness, and intensity parameters of each frequency band. This model then generates anti-eavesdropping interference noise tailored to the current audio. For example, if the ambient audio has a high percentage of low-frequency energy, the generated interference noise will have a correspondingly higher energy distribution in the low-frequency band, ensuring the targeted and effective interference effect.

[0089] For example, when performing frequency analysis, a Fast Fourier Transform (FFT) can be used to divide the low-frequency band (20Hz-200Hz), mid-frequency band (200Hz-2kHz), and high-frequency band (2kHz-20kHz) into three groups, calculating the energy contribution of each band. For volume analysis, a root mean square (RMS) algorithm combined with dynamic range compression technology is used to accurately determine loudness values, enabling precise monitoring in different volume scenarios. Intensity analysis determines the intensity value based on the amplitude and power characteristics of the audio signal, taking into account factors such as environmental attenuation. This provides a key basis for subsequent interference noise generation.

[0090] Then, after obtaining the analysis results, an interference noise model is constructed based on existing intelligent generation algorithms. For example, if the low-frequency energy of the ambient audio accounts for 40%, the generated interference noise is dynamically adjusted between 35% and 45% of the low-frequency energy distribution. At the same time, the loudness and intensity parameters are adapted based on this method to generate an interference noise signal with a specific spectral shape, loudness change curve, and intensity fluctuation pattern. This ensures that the interference effect covers the critical frequency band while avoiding excessive interference with normal audio perception, achieving the goal of accurately interfering with monitoring equipment.

[0091] Please refer to Figure 6 , further, generating an adaptive interference noise according to the analysis result, further comprising:

[0092] S22. Make the interference noise jump randomly within a predetermined frequency band.

[0093] In this embodiment, the randomness and unpredictability of the interference are enhanced by causing the interference noise to randomly jump within a predetermined frequency band.

[0094] For example, when the ambient audio and / or human voice audio are distributed in the low frequency band, the interference noise is locked in the low frequency band and randomly switches the frequency, loudness and intensity, continuously interfering with the monitoring equipment's capture and analysis of this frequency band; if the audio is concentrated in the mid-frequency band, the interference noise flexibly jumps in the mid-frequency band and the transition area between the high and low frequency bands, fully covering the frequency range that may be monitored; when the audio is in the high frequency band, the interference noise changes randomly in the high frequency band to prevent information leakage in the high frequency band.

[0095] Furthermore, in step S22, the interference noise is caused to randomly jump within a predetermined frequency band, including:

[0096] When the ambient audio and / or human voice audio are distributed in the low frequency band, the interference noise is randomly jumped within the low frequency band;

[0097] When the ambient audio and / or human voice audio are distributed in the mid-frequency band, the interference noise is randomly jumped in the mid-frequency band and in the transition frequency bands between the mid-frequency band and the high frequency band and the low frequency band;

[0098] When the ambient audio and / or human voice audio are distributed in the high frequency band, the interference noise is randomly jumped within the high frequency band.

[0099] Please refer to Figure 7 Furthermore, in step S2, the audio parameters are analyzed, and corresponding interference noise is generated according to the analysis results, further comprising:

[0100] S231, identifying human voice audio information, and when a special timbre exists, identifying a characteristic frequency band of the special timbre;

[0101] S232. Generate interference noise with a reverse spectrum according to the characteristic frequency band of the special timbre.

[0102] It's understandable that during the audio analysis process, if a distinctive timbre is detected in the human voice, its characteristic frequency band is immediately locked, and interference noise with an inverse spectrum is subsequently generated. For example, for a high-pitched, sharp voice, the generated interference noise exhibits opposite spectral characteristics in the corresponding high-frequency band, effectively negating the monitoring device's ability to receive and recognize that distinctive voice. Example 2

[0103] Please refer to Figure 8 Based on the above embodiments, an embodiment of the present invention provides an anti-eavesdropping device for implementing any of the above adaptive anti-eavesdropping methods, including:

[0104] The signal acquisition unit 10 is used to collect audio parameters in the target space when the anti-monitoring function is triggered; the audio parameters include ambient audio information and human voice audio information;

[0105] The analysis and processing unit 20 is used to analyze audio parameters and generate corresponding interference noise according to the analysis results; the interference noise is used to interfere with the monitoring of the monitoring device in the current target space.

[0106] Specifically, the signal acquisition unit 10 includes an audio sensor array for accurately capturing subtle audio fluctuations in the target space to ensure the comprehensiveness and accuracy of audio parameters.

[0107] When the anti-monitoring function is triggered, the signal acquisition unit 10 responds quickly and synchronously acquires the ambient audio information and the human voice audio information according to the preset sampling frequency and accuracy.

[0108] After receiving the audio data, the analysis and processing unit 20 conducts an in-depth analysis of the audio parameters based on advanced digital signal processing algorithms and deep learning models.

[0109] For example, when performing frequency analysis, a fast Fourier transform can be used to divide the low-frequency band (20Hz-200Hz), mid-frequency band (200Hz-2kHz), and high-frequency band (2kHz-20kHz), and the energy contribution of each band can be calculated. For volume analysis, a root mean square algorithm combined with dynamic range compression technology is used to accurately determine loudness values, adapting to precise monitoring in different volume scenarios. Intensity analysis determines the intensity value based on the amplitude and power characteristics of the audio signal, taking into account factors such as environmental attenuation, providing a key basis for subsequent interference noise generation.

[0110] Then, after obtaining the analysis results, an interference noise model is constructed based on existing intelligent generation algorithms. For example, if the low-frequency energy of the ambient audio accounts for 40%, the generated interference noise is dynamically adjusted between 35% and 45% of the low-frequency energy distribution. At the same time, the loudness and intensity parameters are adapted based on this method to generate an interference noise signal with a specific spectral shape, loudness change curve, and intensity fluctuation pattern. This ensures that the interference effect covers the critical frequency band while avoiding excessive interference with normal audio perception, achieving the goal of accurately interfering with monitoring equipment. Example 3

[0111] Based on the foregoing embodiments, an embodiment of the present invention provides a computer device including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the adaptive anti-eavesdropping method in embodiment 1 is implemented. Example 4

[0112] Based on the above embodiments, the present invention provides a storage medium containing computer-executable instructions, and the computer-executable instructions are executed by a computer processor to implement the adaptive anti-eavesdropping method as described in the first embodiment.

[0113] Finally, it should be noted that although the above embodiments have been described in the specification and drawings of this application, this does not limit the scope of patent protection of this application. All technical solutions generated by replacing or modifying equivalent structures or equivalent processes based on the essential concepts of this application using the contents recorded in the specification and drawings of this application, as well as directly or indirectly implementing the technical solutions of the above embodiments in other related technical fields, are included in the scope of patent protection of this application.

Claims

1. An adaptive anti-eavesdropping method, characterized in that: include: When the anti-monitoring function is triggered, the audio parameters in the target space are collected; the audio parameters include ambient audio information and human voice audio information; Analyzing the audio parameters and generating corresponding interference noise according to the analysis results; wherein the interference noise is used to interfere with the monitoring of the monitoring device in the current target space; Collecting voice information in the target space and analyzing whether there are preset monitoring keywords; When there are preset monitoring keywords, the anti-monitoring function is automatically triggered; The analyzing the audio parameters includes: Analyze the ambient audio information and human voice audio information from the three dimensions of sound frequency, volume and intensity, and obtain the energy proportion, loudness value and intensity value of the ambient audio and human voice audio in each frequency band respectively; Generating the corresponding interference noise according to the analysis result includes: Based on the analysis results, the energy proportion parameters, loudness parameters and intensity parameters of the interference noise in each frequency band are allocated to generate interference noise.

2. The adaptive anti-eavesdropping method according to claim 1, characterized in that: Also includes: Use the camera to capture the image of the person in the target space in real time and compare it with the specific person image in the database; When a matching specific person image is identified, the mouth movements of the corresponding specific person are identified to determine whether the specific person is speaking; When it is recognized that the specific person is speaking, the anti-monitoring function is automatically triggered.

3. The adaptive anti-eavesdropping method according to claim 1, characterized in that: Also includes: Continuously monitor the sound signals in the target space, analyze the timbre of the sound signals, and compare them with the voice characteristics of specific people in the database; When a matching specific person's voice characteristics are recognized, the anti-monitoring function is automatically triggered.

4. The adaptive anti-eavesdropping method according to claim 1, wherein: The generating of the adaptive interference noise according to the analysis result also includes: The interference noise is caused to randomly jump within a predetermined frequency band.

5. The adaptive anti-eavesdropping method according to claim 4, characterized in that: The step of causing the interference noise to randomly jump within a predetermined frequency band includes: When the ambient audio and / or human voice audio are distributed in the low frequency band, causing the interference noise to randomly jump within the low frequency band; When the ambient audio and / or human voice audio are distributed in the mid-frequency band, the interference noise is randomly jumped in the mid-frequency band and in the transition frequency bands between the mid-frequency band and the high frequency band and the low frequency band; When the ambient audio and / or human voice audio are distributed in the high frequency band, the interference noise is caused to randomly jump within the high frequency band.

6. The adaptive anti-eavesdropping method according to claim 1, wherein: The step of analyzing the audio parameters and generating corresponding interference noise according to the analysis results includes: Identifying the human voice audio information, and when a special timbre exists, identifying a characteristic frequency band of the special timbre; According to the characteristic frequency band of the special timbre, interference noise with an inverse spectrum is generated.

7. An anti-eavesdropping device, characterized in that: The method for implementing the adaptive anti-eavesdropping method according to any one of claims 1 to 6 comprises: A signal acquisition unit, configured to collect audio parameters in the target space when the anti-monitoring function is triggered; the audio parameters include ambient audio information and human voice audio information; The analysis and processing unit is used to analyze the audio parameters and generate corresponding interference noise according to the analysis results; the interference noise is used to interfere with the monitoring of the monitoring device in the current target space.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the adaptive anti-eavesdropping method according to any one of claims 1 to 6 is implemented.

9. A storage medium containing computer-executable instructions, characterized in that: The computer executable instructions are executed by a computer processor to implement the adaptive anti-eavesdropping method according to any one of claims 1 to 6.

Images