Adaptive White-Box Watermarking with Self-Mutual Check Parameters in Deep Neural Networks

Artificial Intelligence (AI) has found wide application, but also poses risks due to unintentional or malicious tampering during deployment. Regular checks are therefore necessary to detect and prevent such risks. Fragile watermarking is a technique used to identify tampering in AI models. However, previous methods have faced challenges including risks of omission, additional information transmission, and inability to locate tampering precisely. In this paper, we propose a method for detecting tampered parameters and bits, which can be used to detect, locate, and restore parameters that have been tampered with. We also propose an adaptive embedding method that maximizes information capacity while maintaining model accuracy. Our approach was tested on multiple neural networks subjected to attacks that modified weight parameters, and our results demonstrate that our method achieved great recovery performance when the modification rate was below 20%. Furthermore, for models where watermarking significantly affected accuracy, we utilized an adaptive bit technique to recover more than 15% of the accuracy loss of the model.

Decision-based iterative fragile watermarking for model integrity verification

Typically, foundation models are hosted on cloud servers to meet the high demand for their services. However, this exposes them to security risks, as attackers can modify them after uploading to the cloud or transferring from a local system. To address this issue, we propose an iterative decision-based fragile watermarking algorithm that transforms normal training samples into fragile samples that are sensitive to model changes. We then compare the output of sensitive samples from the original model to that of the compromised model during validation to assess the model's completeness.The proposed fragile watermarking algorithm is an optimization problem that aims to minimize the variance of the predicted probability distribution outputed by the target model when fed with the converted sample.We convert normal samples to fragile samples through multiple iterations. Our method has some advantages: (1) the iterative update of samples is done in a decision-based black-box manner, relying solely on the predicted probability distribution of the target model, which reduces the risk of exposure to adversarial attacks, (2) the small-amplitude multiple iterations approach allows the fragile samples to perform well visually, with a PSNR of 55 dB in TinyImageNet compared to the original samples, (3) even with changes in the overall parameters of the model of magnitude 1e-4, the fragile samples can detect such changes, and (4) the method is independent of the specific model structure and dataset. We demonstrate the effectiveness of our method on multiple models and datasets, and show that it outperforms the current state-of-the-art.