The Brittleness of AI-Generated Image Watermarking Techniques: Examining
  Their Robustness Against Visual Paraphrasing Attacks
Authors: name: Niyar R Barman, name: Krish Sharma, name: Ashhar Aziz, name: Shashwat Bajpai, name: Shwetangshu Biswas, name: Vasu Sharma, name: Vinija Jain, name: Aman Chadha, name: Amit Sheth, name: Amitava Das

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.