title: The Brittleness of AI-Generated Image Watermarking Techniques: Examining
    Their Robustness Against Visual Paraphrasing Attacks
summary: 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.
paper_authors._id: 66c59839960ea4e66c672b9b
paper_authors.name: Niyar R Barman
paper_authors._id: 66c59839960ea4e66c672b9c
paper_authors.name: Krish Sharma
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paper_authors.name: Ashhar Aziz
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paper_authors.name: Shashwat Bajpai
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paper_authors.fullname: Shashwat Bajpai
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paper_authors.name: Shwetangshu Biswas
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paper_authors.fullname: Shwetangshu Biswas
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paper_authors.user: shwetangshub
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paper_authors.name: Vasu Sharma
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paper_authors.fullname: Vasu Sharma
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paper_authors.user: vasusharma55
paper_authors._id: 66c59839960ea4e66c672ba1
paper_authors.name: Vinija Jain
paper_authors._id: 66c59839960ea4e66c672ba2
paper_authors.name: Aman Chadha
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paper_authors.fullname: Aman Chadha
paper_authors.type: user
paper_authors.user: amanchadha
paper_authors._id: 66c59839960ea4e66c672ba3
paper_authors.name: Amit Sheth
paper_authors._id: 66c59839960ea4e66c672ba4
paper_authors.name: Amitava Das
paper_title: The Brittleness of AI-Generated Image Watermarking Techniques: Examining
    Their Robustness Against Visual Paraphrasing Attacks
paper_summary: 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.
paper_ai_keywords: text-to-image generation systems Stable Diffusion Midjourney Imagen DALL-E watermarking techniques visual paraphrase attacks image captioning systems KOSMOS-2 image-to-image diffusion system denoising step visual paraphrase robust watermarking techniques