Neural network fragile watermarking with no model performance degradation

• URL: http://arxiv.org/abs/2208.07585v1
• Date: Tue, 16 Aug 2022 07:55:20 GMT
• Title: Neural network fragile watermarking with no model performance degradation
• Authors: Zhaoxia Yin, Heng Yin, and Xinpeng Zhang
• Abstract summary: We propose a novel neural network fragile watermarking with no model performance degradation. Experiments show that the proposed method can effectively detect model malicious fine-tuning with no model performance degradation.
• Score: 28.68910526223425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks are vulnerable to malicious fine-tuning attacks such as data poisoning and backdoor attacks. Therefore, in recent research, it is proposed how to detect malicious fine-tuning of neural network models. However, it usually negatively affects the performance of the protected model. Thus, we propose a novel neural network fragile watermarking with no model performance degradation. In the process of watermarking, we train a generative model with the specific loss function and secret key to generate triggers that are sensitive to the fine-tuning of the target classifier. In the process of verifying, we adopt the watermarked classifier to get labels of each fragile trigger. Then, malicious fine-tuning can be detected by comparing secret keys and labels. Experiments on classic datasets and classifiers show that the proposed method can effectively detect model malicious fine-tuning with no model performance degradation.

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