Method, system and product for end-to-end verification of video stream based on schnorr signature

Abstract

This invention discloses an end-to-end verification method, system, and product for video streams based on Schnorr signatures. The sending end first encodes the audio and video streams into a continuous sequence. For each encoded transmission packet, a point in the group is calculated using random numbers as the random commitment point for the frame signature, where is the generator of the selected elliptic curve group, and the group order is a prime number. Then, a signature scalar is calculated according to the Schnorr signature algorithm. Next, the original video frame data, the random point, and the signature scalar are combined into an authentication data packet, which is pushed to the server and client in real time via a network protocol. Finally, the client performs instant authentication and cache management, while the server performs frame-by-frame verification and aggregated storage. This invention utilizes the linear aggregation characteristics of Schnorr signatures to achieve efficient authentication and traceability of real-time video streams and historical stored data while significantly reducing storage space requirements, ensuring the integrity and evidentiary value of the video data.

Description

Technical Field

[0001] This invention belongs to the field of information security technology and relates to a video stream end-to-end verification method, system and product, specifically a video stream end-to-end verification method, system and product based on Schnorr signature. Background Technology

[0002] In the fields of IoT and video surveillance, ensuring the authenticity and integrity of audio and video stream data has always been the core of security protection. In real-time monitoring scenarios, traditional streaming media transmission often only focuses on encryption to prevent eavesdropping, but neglects reliable authentication of the video frame source, making video data vulnerable to tampering, forgery, or replay attacks. With the popularization of high-definition monitoring, video streams based on H.264 or H.265 encoding have extremely large data volumes, and these streaming media have extremely high requirements for transmission latency and storage efficiency. Unlike ordinary documents or instant messages, video streams are composed of continuous NALUs (Network Extraction Units). If an independent digital signature is attached to each frame of data and persistently stored, it will lead to a huge waste of storage space and put a heavy storage pressure on the backend monitoring server.

[0003] Using aggregated signature technology is an important means to resolve the conflict between authentication of large-scale streaming data and efficient storage. Current audio and video authentication schemes that use traditional RSA or ECDSA algorithms not only have high computational overhead, but also suffer from linear growth of signature information with the number of frames, making it difficult to balance security and system performance. Summary of the Invention

[0004] To address the issues of excessive storage overhead in existing technologies and the difficulty in guaranteeing strong non-repudiation in symmetric authentication schemes such as HMAC, this invention proposes an end-to-end video stream authentication method, system, and product based on Schnorr signatures, leveraging the linear aggregation characteristics of Schnorr signatures. While significantly reducing storage space requirements, it achieves efficient authentication and traceability of the source of real-time video streams and historical stored data, ensuring the integrity and evidentiary value of video data.

[0005] The technical solution adopted by the present invention is: an end-to-end verification method for video streams based on Schnorr signatures, comprising the following steps: Step 1: The sending end encodes the audio and video streams into a continuous sequence. For each encoded transmission packet Using random numbers Computation group Points in , serving as a random commitment point for the frame signature, where To select the elliptic curve group The generators of the group are prime numbers. Calculate the signature scalar based on the Schnorr signature algorithm. ; to convert the original video frame data Random points and signature scalar Combined into an authentication data packet It is pushed to the server and client in real time via network protocol; Step 2: The client performs real-time authentication and cache management, while the server performs frame-by-frame verification and aggregated storage.

[0006] Preferably, step 1 includes the following sub-steps: Step 1.1: The sending end encodes the acquired raw image information into a bitstream, dividing it into continuous NALU sequences. ; Step 1.2: Obtain the sender's private key and the corresponding public key ,in To select the elliptic curve group The generators of the group are prime numbers. ; Step 1.3: For each encoded transmission packet , Generate an independent random number ; Step 1.4: Compute the group Points in This serves as a random commitment point for the frame's signature; Step 1.5: Calculate the challenge value using a cryptographic hash function. Where H() is a hash operation (usually using the SHA256 algorithm). A unique identifier for the sender is used to prevent replay attacks and identity spoofing. It is a combination of steps 1.1 and 1.3 above; Step 1.6: Calculate the signature scalar based on the Schnorr signature algorithm. ; Step 1.7: Extract the original video frame data Random points and signature scalar Combined into an authentication data packet It is pushed to the server and client in real time via network protocols.

[0007] Preferably, in step 1.2, the private key Ideally, it should be stored in the camera's Secure Element (SE) or Trusted Execution Environment (TEE).

[0008] The client performs instant authentication as described in step 2, which specifically includes the following sub-steps: Step 2.1: The client receives real-time data packets. Then, extract video frames. Random points and signature scalar First, verify Is it a group? On the valid points and Is it within the legal scope? Step 2.2: Calculate the local challenge value And verify the equation. The system checks whether the verification is successful. If the verification fails, the frame data is determined to have been tampered with or of unknown origin and is immediately discarded. If the verification succeeds, the client will send the video frame back to the client. It is placed into the playback buffer for decoding and playback.

[0009] Preferably, in the cache management described in step 2, the client only maintains the necessary playback buffer in memory, and once the video frame has finished playing, it is immediately and completely deleted from the cache. Related authentication information is not persistently stored locally to ensure the privacy and security of the playback device.

[0010] Preferably, the server-side frame-by-frame verification and aggregated storage in step 2 is specifically implemented by the following sub-steps: Step 3.1: After receiving the video stream, the server processes each frame... Real-time verification is conducted to ensure that the video streams recorded and stored in the system have legal validity and are non-repudiable. Step 3.2: For a set of verified video frames, the server performs space optimization using the linear additivity of Schnorr signatures in composite groups; Step 3.3: Calculate the set The aggregate scalar of signatures and ; Step 3.4: Persistently store the original video stream on the server side. Random point sequences corresponding to each frame and the unique aggregate signature .

[0011] As a preferred option, when a third-party organization or user needs to verify the authenticity of stored historical videos, the server retrieves the corresponding video frame sequence from the database. Random point sequence and aggregate signature The auditors recalculated the hash challenge value for each frame based on the acquired data. Calculate the sum of all random points. Calculate the cumulative effect of the challenge value multiplied by the public key; During verification, the auditor checks whether the following aggregation equation holds true: ; If the equation holds true, it proves that every frame of the entire video stream has not been tampered with since it was captured and uploaded, and that the source of the entire stream is indeed identified as [identified by the source]. The camera.

[0012] The technical solution adopted by the system of this invention is: an end-to-end verification system for video streams based on Schnorr signatures, comprising: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the described end-to-end verification method for video streams based on Schnorr signatures.

[0013] The technical solution adopted by the product of the present invention is: an end-to-end verification product for video streams based on Schnorr signatures, comprising computer program instructions, which, when the computer program instructions are run on a computer, cause the computer to execute the end-to-end verification method for video streams based on Schnorr signatures.

[0014] The present invention also provides a non-volatile computer-readable storage medium containing a computer program that, when executed by one or more processors, causes the processors to perform the described end-to-end verification method for video streams based on Schnorr signatures.

[0015] Compared with the prior art, the advantages of the present invention include: (1) The present invention can ensure the non-repudiation of video stream during transmission by using a private key to sign each H.264 / H.265 NALU in real time at the camera end and performing double verification on the server and client.

[0016] (2) The client of this invention only maintains the necessary playback buffer in memory. Once the video frame has finished playing, it is immediately and completely deleted from the cache. Related authentication information is not persistently stored locally to ensure the privacy and security of the playback device.

[0017] (3) In this invention, the server no longer needs to store a 256-bit signature scalar for each frame separately. Instead, only one aggregated form remains. In long-term video storage scenarios, it can significantly reduce the storage overhead of metadata and improve the utilization rate of storage resources in long-term storage scenarios.

[0018] (4) This invention achieves a high degree of unity between streaming data authentication and storage efficiency without sacrificing security strength, and is suitable for large-scale security monitoring and live broadcast protection.

[0019] (5) The present invention adopts independent frame-based authentication logic, which ensures the continuity of authentication in non-ideal communication environments such as 4G / 5G. Single frame packet loss will not affect the verification of subsequent frames, effectively avoiding playback stuttering and authentication failure caused by network jitter, and ensuring a real-time viewing experience.

[0020] (6) This invention has extremely low resource consumption on the system's core CPU / GPU. In security hubs or large-scale live streaming scenarios with massive concurrent video stream access, it can provide high throughput and low latency security, and has strong engineering application value. Attached Figure Description

[0021] The technical solutions of the present invention will be further illustrated below using embodiments and specific implementation methods. In addition, some accompanying drawings are used in the description of the technical solutions. Those skilled in the art can obtain other drawings and the intent of the present invention from these drawings without any creative effort.

[0022] Figure 1 This is a flowchart illustrating the real-time signing and transmission process of audio and video streams by the sending end (camera capturing end) in an embodiment of the present invention. Figure 2 This is a flowchart illustrating the client-side real-time authentication and cache management, and the server-side frame-by-frame verification and aggregated storage process in this embodiment of the invention. Detailed Implementation

[0023] To facilitate understanding and implementation of the present invention by those skilled in the art, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0024] Aggregated signature technology is an important means to resolve the conflict between authentication and efficient storage for large-scale streaming data. Current audio and video authentication schemes using traditional RSA or ECDSA algorithms not only incur high computational costs, but also suffer from linear growth of signature information with the number of frames, making it difficult to balance security and system performance. The Schnorr signature algorithm, with its unique linearity and aggregation characteristics, provides a new solution for this scenario.

[0025] This embodiment provides an end-to-end verification method for video streams based on Schnorr signatures, including the following steps: Step 1: The sending end (camera capturing end) signs and sends the audio and video streams in real time; in one implementation, please see... Figure 1The specific implementation of step 1 includes the following sub-steps: (1a) The camera, as the data producer, acquires raw image information through the audio and video acquisition module, and uses the built-in hardware encoder to encode the H.264 or H.265 bitstream, dividing it into a continuous NALU (Network Extraction Unit) sequence. .

[0026] (1b) Initialize security parameters: Obtain the camera's private key and the corresponding public key ,in To select the elliptic curve group The generators of the group are prime numbers. The private key Stored in the camera's Secure Element (SE) or Trusted Execution Environment (TEE).

[0027] (1c) For each encoded transmission packet ( Generate an independent random number. .

[0028] (1d) Computational group Points in This serves as a random commitment point for the frame signature.

[0029] (1e) Calculate the challenge value using a cryptographic hash function (such as SM3 or SHA-256). H() is a hash operation (usually using the SHA256 algorithm). A unique identifier for the sender is used to prevent replay attacks and identity spoofing. It is a combination of steps 1.1 and 1.3 above; (1f) Calculate the signature scalar according to the Schnorr signature algorithm ; (1g) Encapsulating the data payload: Encapsulating the original video frame data Random points and signature scalar Combined into an authentication data packet It is pushed to the server and client in real time via network protocols.

[0030] Step 2: Real-time authentication and cache management on the client (real-time playback end), and frame-by-frame verification and aggregated storage on the server (storage center); In one implementation, please see Figure 2 The real-time authentication and cache management of the client (real-time playback end) mentioned in step 2 is specifically implemented through the following sub-steps: (2a) The client receives real-time data packets Then, extract video frames. Random points and signature scalar First, verify Is it a group? On the valid points and Whether it is within the legal scope.

[0031] (2b) Calculate the local challenge value And verify the equation. Check if the verification is valid. If the verification fails, the frame data is determined to have been tampered with or of unknown origin and is immediately discarded.

[0032] (2c) If the verification is successful, the client will send the video frames. It is placed into the playback buffer for decoding and playback.

[0033] The client only maintains the necessary playback buffer in memory, and once a frame of video has finished playing, it is immediately and completely removed from the cache. Related authentication information is not persistently stored locally to ensure the privacy and security of the playback device.

[0034] The frame-by-frame verification and aggregated storage described in step 2 on the server side (storage center) specifically includes the following sub-steps: (3a) After receiving the video stream, the server processes each frame Perform real-time signature verification according to the logic of step (2b) to ensure that the video stream recorded in the storage system has legal validity and is non-repudiable.

[0035] (3b) For a set of video frames that have passed verification (e.g., a GOP sequence or a video segment of fixed duration), the server uses the linear aggregation property of the Schnorr signature to perform spatial optimization.

[0036] (3c) Calculate the group The aggregate scalar sum of the signatures: .

[0037] (3d) Implement storage slimming strategy: The server persistently stores the original video stream. Random point sequences corresponding to each frame and the unique aggregate signature .

[0038] Since the server no longer needs to store a 256-bit signature scalar for each frame separately. Instead, only one aggregated form remains. In long-term video storage scenarios, it can significantly reduce the storage overhead of metadata and improve the utilization rate of storage resources in long-term storage scenarios.

[0039] In one implementation, when a third-party organization or user needs to verify the authenticity of stored historical videos, the server retrieves the corresponding video frame sequence from the database. Random point sequence and aggregate signature The auditors recalculated the hash challenge value for each frame based on the data they obtained. Calculate the sum of all random points. Calculate the cumulative effect of the challenge value multiplied by the public key.

[0040] During verification, the auditor checks whether the following aggregation equation holds true:

[0041] If the equation holds true, it proves that every frame of the entire video stream has not been tampered with since it was captured and uploaded, and that the source of the entire stream is indeed identified as [identified by the source]. The camera.

[0042] The following specific experiments further verify the solution of the present invention.

[0043] To verify the effectiveness of the proposed Schnorr signature-based end-to-end authentication and aggregation storage method for audio and video streams, this experimental environment was built on a high-performance x86 computing platform. Instead of using physical embedded camera hardware, the experiment implemented a complete closed-loop simulation of audio and video acquisition, encoding, streaming, pulling, and encryption / decryption authentication logic within a Linux simulation environment. In this simulation system, the sending end simulates the camera recording process, uses the FFmpeg toolset for H.264 and H.265 stream encapsulation, and combines a custom C language encryption component to generate the Schnorr signature. The server is responsible for simulating the operation of streaming media distribution and aggregation signature storage logic, while the receiving end simulates the signature verification and playback process of a real-time player. Through this simulation configuration, the experiment can precisely control network parameters and quantify the algorithm's execution efficiency on a high-performance processor.

[0044] Table 1: Experimental Environmental Parameters

[0045] The experiment was conducted in a simulated real-world security monitoring and streaming media distribution environment. The experimental environment consisted of three parts: a camera, a server, and a playback client. The camera used an embedded ARM development board with integrated hardware encoding modules to simulate an industrial-grade camera. The server used a high-performance x86 architecture server for storage and data management. The playback client used a mainstream mobile device. On the software side, the system was based on a Linux operating system, utilizing the FFmpeg open-source framework for H.264 stream acquisition, NALU unit decomposition and encapsulation, and calling an optimized elliptic curve algorithm library to perform Schnorr signature operations. Representative 1080P high-bitrate video streams and 480P standard-bitrate video streams were selected for the experiment, with each GOP length set to 30 frames to fully compare the impact of authentication information on bandwidth and storage at different resolutions.

[0046] In the testing process, because this invention adopts a strategy combining frame-by-frame signing and GOP aggregation storage, the camera attaches an independent Schnorr signature scalar when sending each frame NALU. When packet loss occurs and a specific frame is missing, the client only needs to perform independent verification based on the currently received single-frame data, thereby ensuring the real-time performance and anti-interference capability of the video stream playback. The server, after fully receiving and verifying the legality of all frames within a GOP, executes the signature aggregation logic to ensure that the stored records are logically complete and non-repudiable.

[0047] In a comparative experiment on storage costs, a group of images (GOP) was used as the basic comparison unit. The increase in storage usage as video duration accumulated was monitored, comparing the traditional Schnorr signature frame-by-frame storage scheme with the aggregated storage scheme of this invention. In the traditional scheme, each frame of video data requires an additional 32-byte random commitment point R and a 32-byte signature s, resulting in an additional 64 bytes of metadata overhead per frame. In contrast, this invention, during the aggregated storage stage, accumulates and merges all signature s within a GOP into a single 32-byte aggregated scalar, significantly reducing the average additional overhead per frame. Experiments testing different resolutions show that for 1080P, 640P, and 480P video streams, as the number of GOPs accumulates, the overhead for signature information storage in this scheme is consistently reduced by approximately 49% compared to the traditional scheme. Although the increased size of the video stream itself leads to a decrease in the proportion of signature information in the total data volume due to higher resolution and bitrate, this scheme still demonstrates extremely high space optimization value for 1080P video streams in scenarios involving massive historical data storage.

[0048] In the latency test experiment, the impact of Schnorr signing and verification operations on live streaming and real-time playback latency was evaluated. The total latency introduced by the authentication process was calculated by recording the average time from encoding completion to signature generation at the camera end and the average time from data reception to successful verification at the client end. Experimental data showed that in all resolution test groups, the signing time per frame was controlled within 1.2 milliseconds, and the client-side verification time per frame was approximately 0.9 milliseconds. This millisecond-level latency was evaluated within the overall live streaming latency context. Since mainstream live streaming systems such as RTMP or HLS typically have end-to-end latency on the order of seconds, the microsecond-level computational overhead introduced by this invention accounts for only a very small proportion of the total latency. The experimental results ultimately demonstrate that this solution can meet the requirements of strong non-repudiation authentication while successfully balancing efficient utilization of storage resources and high-performance real-time transmission in different application scenarios such as 1080P, 720P, and 480P, without affecting the user's viewing experience.

[0049] In storage statistics, the two encoding standards, H.265 and H.264, have various frame types, generally categorized as I-frames, P-frames, and B-frames. I-frames (keyframes) are much larger than P / B frames, but the total size of a GOP (Group of Pictures) is determined by the bitrate. When calculating average storage percentage, the average value is used for statistical convenience.

[0050] Test video stream data at different bitrates and store the cost table. The differences between the two schemes are shown in Tables 2 and 3 at different bitrates and resolutions. Table 2: Comparison of storage overhead at different resolutions under the H.264 (AVC) standard (FPS=30, GOP=30)

[0051] Note: Bold numbers represent the optimal result; the ordinary solution is to add a signature to each NALU; the aggregation solution of this invention is to store the aggregate signature during storage. Table 3: Comparison of storage overhead at different resolutions under the H.265 (HEVC) standard (FPS=30, GOP=30)

[0052] Note: Bold numbers represent the optimal result; the ordinary solution is to add a signature to each NALU; the aggregation solution of this invention is to store the aggregate signature during storage. Based on real bandwidth data from current mainstream communication networks and audio / video coding standards, an in-depth quantitative analysis of end-to-end latency was conducted. According to the ITU-T G.114 standard's evaluation system for one-way end-to-end latency, latency within 150ms is defined as a high-level user satisfaction range. Real-world test data from a high-performance x86 simulation platform (i7-11700) and mainstream 4G / 5G network environments show that the total link latency after adding Schnorr signature authentication remains stable between 30ms and 110ms, far below the 300ms threshold at which the human eye perceives noticeable delay, and within the 150ms excellent performance range defined by the ITU standard. This demonstrates that this invention provides high-strength security authentication while completely preserving the real-time performance and smoothness of the original audio and video streams.

[0053] Please see Table 4 for current mainstream general network bandwidth data; Table 4: Bandwidth data in real-world network environments

[0054] Tables 5 and 6 show the perceived delay margin for the human eye, calculated based on the specific computational delay in the simulation environment. According to the margins below and the commonly used bandwidths in Table 4, it can be found that they can meet the visual delay conditions.

[0055] Table 5: End-to-End Delay Decomposition under H.264 Standard

[0056] Table 6: End-to-End Delay Breakdown under H.265 Standard

[0057] This invention utilizes the linear aggregation properties of Schnorr signatures to achieve efficient authentication and traceability of real-time video streams and historical stored data while significantly reducing storage space requirements, thus ensuring the integrity and evidentiary value of video data.

[0058] It should be understood that the embodiments described above are only some, not all, of the embodiments of the present invention. Furthermore, the technical features of the various embodiments or individual embodiments provided by the present invention can be arbitrarily combined to form feasible technical solutions. Such combinations are not constrained by the order of steps and / or structural composition patterns, but must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0059] It should be understood that the above description of the preferred embodiments is quite detailed, but it should not be considered as a limitation on the scope of protection of this invention. Those skilled in the art, under the guidance of this invention, can make substitutions or modifications without departing from the scope of protection of the claims of this invention, and all such substitutions or modifications fall within the scope of protection of this invention. The scope of protection of this invention should be determined by the appended claims.

Claims

1. A video stream end-to-end verification method based on Schnorr signature, characterized in that, Includes the following steps: Step 1: The sending end encodes the video stream into a continuous sequence. For each encoded transmission packet Using random numbers Computational group Points in , serving as a random commitment point for the frame signature, where To select the elliptic curve group The generators of the group are prime numbers. Calculate the signature scalar based on the Schnorr signature algorithm. ; to convert the original video frame data Random points and signature scalar Combined into an authentication data packet The data is pushed to the server and client in real time via network protocol; Step 2: The client performs instant authentication and cache management, and the server verifies and aggregates the data frame by frame.

2. The method of claim 1, wherein the method further comprises: Step 1 includes the following sub-steps: Step 1.1: The sender encodes the original image information obtained, and codes the video stream to divide it into a continuous NALU sequence ; Step 1.2: Obtain the sender's private key and the corresponding public key ,in To select the elliptic curve group The generators of the group are prime numbers. ; Step 1.3: For each encoded transmission packet , Generate an independent random number ; Step 1.4: Compute the group Points in This serves as a random commitment point for the frame's signature; Step 1.5: Calculate the challenge value using a cryptographic hash function. Where H() is the hash operation. A unique identifier for the sender is used to prevent replay attacks and identity spoofing. It is a combination of steps 1.1 and 1.3 above; Step 1.6: Calculate the signature scalar based on the Schnorr signature algorithm. ; Step 1.7: Extract the original video frame data Random points and signature scalar Combined into an authentication data packet It is pushed to the server and client in real time via network protocols.

3. The end-to-end verification method for video streams based on Schnorr signatures according to claim 2, characterized in that, In step 1.2, the private key is stored in a secure element SE or a trusted execution environment TEE of the camera.

4. The end-to-end verification method for video streams based on Schnorr signatures according to claim 2, characterized in that, The client performs instant authentication as described in step 2, which specifically includes the following sub-steps: Step 2.1: The client receives real-time data packets. Then, extract video frames. Random points and signature scalar First, verify Is it a group? On the valid points and Is it within the legal range? Step 2.2: Calculate the local challenge value. And verify the equation. If the verification fails, the frame data is determined to have been tampered with or of unknown origin and is immediately discarded. If the verification is successful, the client will send the video frames. It is placed into the playback buffer for decoding and playback.

5. The end-to-end verification method for video streams based on Schnorr signatures according to claim 2, characterized in that: The cache management described in step 2 involves the client maintaining only the necessary playback buffer in memory. Once a video frame has finished playing, it is immediately and completely removed from the cache. Related authentication information is not persistently stored locally to ensure the privacy and security of the playback device.

6. The method of claim 2, wherein the method further comprises: The server-side frame-by-frame verification and aggregated storage described in step 2 are specifically implemented through the following sub-steps: Step 3.1: After receiving the video stream, the server processes each frame... Real-time verification is conducted to ensure that the video streams recorded and stored in the system have legal validity and are non-repudiable. Step 3.2: For a set of verified video frames, the server performs space optimization using the linear additivity of the Schnorr signature equation in composite groups; Step 3.3: Calculate the set The aggregate scalar of signatures and ; Step 3.4: Persistently store the original video stream on the server side. Random point sequences corresponding to each frame and the unique aggregate signature .

7. The end-to-end verification method for video streams based on Schnorr signatures according to any one of claims 1-6, characterized in that: When a third-party organization or user needs to verify the authenticity of stored historical videos, the server retrieves the corresponding video frame sequence from the database. Random point sequence and aggregate signature The auditors recalculated the hash challenge value for each frame based on the acquired data. Calculate the sum of all random points. Calculate the cumulative effect of the challenge value multiplied by the public key; During verification, the auditor checks whether the following aggregation equation holds true: ； If the equation holds true, it proves that every frame of the entire video stream has not been tampered with since it was captured and uploaded, and that the source of the entire stream is indeed identified as [identified by the source]. The camera.

8. An end-to-end verification system for video streams based on Schnorr signatures, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the end-to-end verification method for video streams based on Schnorr signatures as described in any one of claims 1 to 7.

9. An end-to-end verification product for video streams based on Schnorr signatures, comprising computer program instructions, characterized in that: When the computer program instructions are executed on a computer, the computer performs the end-to-end verification method for video streams based on Schnorr signatures as described in any one of claims 1 to 7.

10. A non-volatile computer-readable storage medium containing a computer program, characterized in that: When the computer program is executed by one or more processors, the processors perform the end-to-end verification method for video streams based on Schnorr signatures as described in any one of claims 1 to 7.

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