The Brittleness of AI-Generated Image Watermarking Techniques: Examining Their Robustness Against Visual Paraphrasing Attacks
<|reference_start|>The Brittleness of AI-Generated Image Watermarking Techniques: Examining Their Robustness Against Visual Paraphrasing Attacks: The rapid advancement of text-to-image generation systems, exemplified by models like Stable Diffusion, Midjourney, Imagen, and DALL-E, has heightened concerns about their potential misuse. In response, companies like Meta and Google have intensified their efforts to implement watermarking techniques on AI-generated images to curb the circulation of potentially misleading visuals. However, in this paper, we argue that current image watermarking methods are fragile and susceptible to being circumvented through visual paraphrase attacks. The proposed visual paraphraser operates in two steps. First, it generates a caption for the given image using KOSMOS-2, one of the latest state-of-the-art image captioning systems. Second, it passes both the original image and the generated caption to an image-to-image diffusion system. During the denoising step of the diffusion pipeline, the system generates a visually similar image that is guided by the text caption. The resulting image is a visual paraphrase and is free of any watermarks. Our empirical findings demonstrate that visual paraphrase attacks can effectively remove watermarks from images. This paper provides a critical assessment, empirically revealing the vulnerability of existing watermarking techniques to visual paraphrase attacks. While we do not propose solutions to this issue, this paper serves as a call to action for the scientific community to prioritize the development of more robust watermarking techniques. Our first-of-its-kind visual paraphrase dataset and accompanying code are publicly available.<|reference_end|>
@article{barman2024the,
    title={The Brittleness of AI-Generated Image Watermarking Techniques: Examining
  Their Robustness Against Visual Paraphrasing Attacks},
    author={Niyar R Barman, Krish Sharma, Ashhar Aziz, Shashwat Bajpai,
  Shwetangshu Biswas, Vasu Sharma, Vinija Jain, Aman Chadha, Amit Sheth and
  Amitava Das},
    journal={arXiv preprint arXiv:2408.10446},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10446},
    primaryClass={cs.CV cs.AI}
}