Abstract: With the increasing awareness of citizen copyright, more and more images containing watermarks are appearing in daily life. However, existing research shows that images with watermarks can cause neural network misclassification, posing a significant threat to the popularization and application of neural networks. Adversarial training is one of the defensive methods to solve this problem, but it requires a large number of watermark adversarial samples as training data. To address this issue, this paper proposes a visible watermark adversarial attack method based on intelligent evolutionary algorithm to generate high-intensity watermark adversarial samples. This method can not only quickly generate watermark adversarial samples, but also maximize the attack on the neural network. In addition, this method incorporates image quality evaluation metrics to constrain the visual loss of the image, making the watermark adversarial samples more visually appealing. The comprehensive experimental results show that the proposed method has lower time complexity than the benchmark watermark attack method, and has a higher attack rate on neural networks compared to the benchmark black box attack.

Key words: adversarial attack, watermark, image quality evaluation, optimization, neural network