Summary of Watermas: Sharpness-aware Maximization For Neural Network Watermarking, by Carl De Sousa Trias et al.
WaterMAS: Sharpness-Aware Maximization for Neural Network Watermarking
by Carl De Sousa Trias, Mihai Mitrea, Attilio Fiandrotti, Marco Cagnazzo, Sumanta Chaudhuri, Enzo Tartaglione
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel watermarking method called WaterMAS is proposed to protect intellectual property rights (IPR) of deep neural networks. This white-box approach enhances the trade-off between robustness, imperceptibility, and computational complexity while allowing for increased data payload and security. The method inserts a watermark into the neural network’s weights during training, making it difficult to alter the model’s performance even with small changes. The relationship between the watermark’s properties is discussed, including its impact on robustness, imperceptibility, and data payload. The security of WaterMAS is evaluated by simulating an attacker intercepting the secret key, which is randomly chosen through multiple layers of the model. Experimental validations are performed on five models (VGG16, ResNet18, MobileNetV3, SwinT) for two tasks (CIFAR10 image classification and Cityscapes image segmentation), as well as four types of attacks (Gaussian noise addition, pruning, fine-tuning, and quantization). The code will be released open-source upon article acceptance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Watermarks are like secret messages that can be added to neural networks to protect them. This new method, called WaterMAS, makes it hard for someone to change the network without detecting the watermark. It’s like hiding a message inside the network’s weights, and even small changes would reveal the message. The researchers tested this method on different types of attacks and found that it works well. They also made sure that the watermark doesn’t affect how the network performs its tasks. |
Keywords
» Artificial intelligence » Fine tuning » Image classification » Image segmentation » Neural network » Pruning » Quantization
