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.
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Niyar R Barman
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Krish Sharma
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Ashhar Aziz
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Shashwat Bajpai
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2025-02-07T16:17:07.439Z
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Shashwat Bajpai
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Shwetangshu Biswas
claimed_verified
2025-03-02T20:19:42.997Z
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Shwetangshu Biswas
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shwetangshub
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Vasu Sharma
claimed_verified
2025-01-10T08:22:03.598Z
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Vasu Sharma
user
vasusharma55
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Vinija Jain
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Aman Chadha
claimed_verified
2024-08-22T19:50:46.730Z
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Aman Chadha
user
amanchadha
66c59839960ea4e66c672ba3
Amit Sheth
66c59839960ea4e66c672ba4
Amitava Das
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.
Visual paraphrase attacks using state-of-the-art captioning and diffusion systems can effectively remove watermarks from AI-generated images, highlighting the vulnerability of existing watermarking techniques.
KOSMOS-2 image captioning image-to-image diffusion system denoising visual paraphrase attacks visual paraphrase dataset