Neural Network Verification via Sentinel Watermarking
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Solution Overview
Problem
Existing neural networks face challenges in verifying authorized usage and preventing unauthorized copies, especially when accessed outside secure networks, leading to difficulties in detecting and addressing potential unauthorized operations.
Innovation Solution
Embedding a watermark within the neural network by training it with a sentinel data set that responds to a predefined query set with an identification signal, and enhancing this signal with an error correcting code to deter modifications and ensure detection of unauthorized copies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a neural network is made accessible outside secure networks, then network performance and accessibility are improved, but vulnerability to unauthorized copying increases
Solution Approach 1:
The patent applies preliminary action by embedding watermarks into the neural network weights before deployment. This proactive measure ensures that even if the network is copied outside secure networks, the copied version will contain detectable watermarks, allowing verification of authorized usage without restricting accessibility.
Solution Approach 2:
The patent uses watermarks as an intermediary mechanism to verify authorized usage. The watermark embedded in the weights acts as a mediator that allows the system to distinguish between authorized and unauthorized copies, enabling protection without preventing legitimate external access to the neural network.
2Reliability
If verification mechanisms are implemented to detect unauthorized copies, then protection capability is improved, but system complexity increases
Solution Approach 1:
The patent extracts the verification function into a separate component that checks for watermarks in the neural network weights. This separation allows the verification mechanism to operate independently, reducing the complexity burden on the core neural network while maintaining strong detection capabilities through dedicated watermark verification logic.
Solution Approach 2:
The patent uses watermark copying as a verification mechanism. By embedding unique watermark patterns into authorized copies of the neural network weights, the system can verify authenticity by checking for these copied watermark signatures, providing simple yet effective detection without complex verification protocols.
3Reliability
If watermarks are embedded in neural network weights, then unauthorized copy detection is improved, but susceptibility to modification attacks increases
Solution Approach 1:
The patent applies parameter changes by using error correcting codes to transform the watermark data structure. This encoding changes the parameters of the watermark representation, enabling the system to detect and correct certain modifications while maintaining accurate copy detection, thus reducing vulnerability to modification attacks.
Solution Approach 2:
The patent implements beforehand cushioning by incorporating error correcting codes into the watermark structure before deployment. This preparatory measure provides a buffer against potential modification attacks, allowing the verification system to tolerate certain levels of corruption or tampering while still accurately detecting unauthorized copies.
Data Source
AI summary
Systems and methods associated with neural network verification are disclosed. One example method may be embodied on a non-transitory computer-readable medium storing computer-executable instructions. The instructions, when executed by a computer, may cause the computer to train a neural network with a training data set to perform a predefined task. The instructions may also cause the computer to train the neural network with a sentinel data set. The sentinel data set may cause the neural network to provide an identification signal in response to a predefined query set. The instructions may also cause the computer to verify whether a suspicious service operates an unauthorized copy of the neural network. The suspicious service may be verified by extracting the identification signal from responses the suspicious service provides to the predefined query set.