Evading Watermark based Detection of AI-Generated Content

• URL: http://arxiv.org/abs/2305.03807v5
• Date: Wed, 8 Nov 2023 15:23:10 GMT
• Title: Evading Watermark based Detection of AI-Generated Content
• Authors: Zhengyuan Jiang, Jinghuai Zhang, Neil Zhenqiang Gong
• Abstract summary: A generative AI model can generate extremely realistic-looking content. Watermark has been leveraged to detect AI-generated content. A content is detected as AI-generated if a similar watermark can be decoded from it.
• Score: 45.47476727209842
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A generative AI model can generate extremely realistic-looking content, posing growing challenges to the authenticity of information. To address the challenges, watermark has been leveraged to detect AI-generated content. Specifically, a watermark is embedded into an AI-generated content before it is released. A content is detected as AI-generated if a similar watermark can be decoded from it. In this work, we perform a systematic study on the robustness of such watermark-based AI-generated content detection. We focus on AI-generated images. Our work shows that an attacker can post-process a watermarked image via adding a small, human-imperceptible perturbation to it, such that the post-processed image evades detection while maintaining its visual quality. We show the effectiveness of our attack both theoretically and empirically. Moreover, to evade detection, our adversarial post-processing method adds much smaller perturbations to AI-generated images and thus better maintain their visual quality than existing popular post-processing methods such as JPEG compression, Gaussian blur, and Brightness/Contrast. Our work shows the insufficiency of existing watermark-based detection of AI-generated content, highlighting the urgent needs of new methods. Our code is publicly available: https://github.com/zhengyuan-jiang/WEvade.

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