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[2602.22197] Off-The-Shelf Image-to-Image Models Are All You Need To Defeat Image Protection Schemes
Summary
This paper demonstrates that off-the-shelf image-to-image models can effectively defeat various image protection schemes, highlighting a significant vulnerability in current defenses.
Why It Matters
As generative AI continues to evolve, understanding its implications for image protection is crucial. This research reveals that many existing protection methods are inadequate, necessitating a reevaluation of security measures in digital image management. The findings emphasize the need for robust defenses against easily accessible AI tools.
Key Takeaways
- Off-the-shelf image-to-image models can act as effective denoisers against image protection schemes.
- The study reveals vulnerabilities in existing image protection methods, which may provide a false sense of security.
- Future protection mechanisms must be benchmarked against attacks using readily available generative AI tools.
- The research includes eight case studies across six different protection schemes.
- The findings call for urgent development of more robust image protection strategies.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.22197 (cs) [Submitted on 25 Feb 2026] Title:Off-The-Shelf Image-to-Image Models Are All You Need To Defeat Image Protection Schemes Authors:Xavier Pleimling, Sifat Muhammad Abdullah, Gunjan Balde, Peng Gao, Mainack Mondal, Murtuza Jadliwala, Bimal Viswanath View a PDF of the paper titled Off-The-Shelf Image-to-Image Models Are All You Need To Defeat Image Protection Schemes, by Xavier Pleimling and 6 other authors View PDF HTML (experimental) Abstract:Advances in Generative AI (GenAI) have led to the development of various protection strategies to prevent the unauthorized use of images. These methods rely on adding imperceptible protective perturbations to images to thwart misuse such as style mimicry or deepfake manipulations. Although previous attacks on these protections required specialized, purpose-built methods, we demonstrate that this is no longer necessary. We show that off-the-shelf image-to-image GenAI models can be repurposed as generic ``denoisers" using a simple text prompt, effectively removing a wide range of protective perturbations. Across 8 case studies spanning 6 diverse protection schemes, our general-purpose attack not only circumvents these defenses but also outperforms existing specialized attacks while preserving the image's utility for the adversary. Our findings reveal a critical and widespread vulnerability in the current landscape of image protection, indicating that many sch...