Neural Network Watermarking via Adversarial Backdoor Triggers
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Solution Overview
Problem
Current watermarking techniques for neural networks are ineffective in establishing ownership, particularly in natural language processing tasks, as they are not easily adaptable from image classification methods and lack robustness against adversarial attacks and model modification attacks.
Innovation Solution
A robust watermarking mechanism is proposed that utilizes adversarial examples and backdoors to create a trigger set, ensuring that each layer of the neural network is marked with watermarks, making it difficult for adversaries to replicate or remove them, while maintaining the model's functionality and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If watermarking techniques from image classification are applied to natural language processing tasks, then proof of ownership can be established, but the techniques lack robustness against adversarial attacks and model modification attacks
Solution Approach 1:
The patent transforms the watermarking approach by changing the fundamental parameters of how watermarks are embedded. Instead of using traditional watermarking methods, it embeds backdoor triggers during the training phase by modifying the training data with adversarial examples. This parameter change makes the watermark inherently robust against removal while adapting to different NLP tasks through the flexibility of trigger generation.
Solution Approach 2:
The patent applies preliminary action by embedding the watermark (backdoor trigger) during the training phase before the model is deployed. The trigger patterns are injected into the training data beforehand, ensuring that the model learns to recognize them as part of its fundamental behavior. This preliminary embedding makes subsequent removal or modification of the watermark extremely difficult without retraining the model.
2Reliability
If backdoors are embedded in each layer of the neural network, then the watermark becomes unremovable and unforgeable, but the complexity of the watermarking process increases
Solution Approach 1:
The patent applies segmentation by dividing the watermark embedding process into layer-specific operations. Each layer of the neural network is watermarked independently with its own set of trigger patterns, ensuring that the watermark is distributed throughout the model architecture. This segmentation makes the watermark unremovable without affecting the entire model, while the modular approach actually simplifies the overall process by making it systematic rather than monolithic.
3Measurement precision
If adversarial examples are generated using structured perturbation, then the trigger set becomes more effective for watermarking, but the computational cost and time required increases
Solution Approach 1:
The patent applies partial action by generating adversarial examples only for a subset of the training data rather than the entire dataset. By selecting representative samples and generating triggers for those, the method achieves effective watermarking while significantly reducing computational overhead. This partial approach maintains measurement precision (trigger effectiveness) while accepting that not all data points need to be processed exhaustively.
Data Source
AI summary
Disclosed herein is a system for watermarking a neural network, comprising memory; and at least one processor in communication with the memory; wherein the memory stores instructions for causing the at least one processor to carry out a method comprising: generating a trigger set by obtaining examples from a training set by random sampling from the training set, respective examples being associated with respective true classes of a plurality of classes; generating a set of adversarial examples by structured perturbation of the examples; generating, for each adversarial example, one or more adversarial class labels by passing the adversarial example to the neural network; and applying one or more trigger labels to each said adversarial example, wherein the one or more trigger labels are selected randomly from the plurality of classes, and wherein each trigger label is not a said true class label for the corresponding example or a said adversarial class label for the corresponding adversarial example; and storing the adversarial examples and corresponding trigger labels as the trigger set; and performing a tuning process to adjust parameters at each layer of the neural network using the trigger set, to thereby generate a watermarked neural network.