CSGuard: Toward Forgery-Resistant Watermarking in Diffusion Models via Compressed Sensing Constraint
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914526345035776 |
|---|---|
| author | Lai, Jiewei Zhang, Lan Tang, Chen Sun, Pengcheng Zhang, Zhaopeng Wang, Yunhao Jin, Hui |
| author_facet | Lai, Jiewei Zhang, Lan Tang, Chen Sun, Pengcheng Zhang, Zhaopeng Wang, Yunhao Jin, Hui |
| contents | Latent-based diffusion model watermarking embeds watermarks into generated images' latent space to enable content attribution, offering a training-free solution for intellectual property protection and digital forensics. However, these methods exhibit a critical vulnerability to the forgery attack, attackers can extract the watermark by inverting the watermarked image and re-generating it with an arbitrary prompt, thereby enabling false attribution on malicious content. In this paper, we propose the CSGuard, the first forgery-resistant watermarking schema that leverages compressed sensing to bind the watermarked image generation and verification to a secret matrix. This ensures that only users possessing the secret matrix can correctly embed or verify the image watermark, prevents the illegal users from forgery without compromising generation quality and watermark integrity. Experimental results demonstrate that CSGuard achieves strong forgery resistance, reduces the attack success rate from 100.0\% to 28.12\%, and achieve 100\% detection rate on benign watermarked images without compromising watermarking effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_01479 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CSGuard: Toward Forgery-Resistant Watermarking in Diffusion Models via Compressed Sensing Constraint Lai, Jiewei Zhang, Lan Tang, Chen Sun, Pengcheng Zhang, Zhaopeng Wang, Yunhao Jin, Hui Computer Vision and Pattern Recognition Latent-based diffusion model watermarking embeds watermarks into generated images' latent space to enable content attribution, offering a training-free solution for intellectual property protection and digital forensics. However, these methods exhibit a critical vulnerability to the forgery attack, attackers can extract the watermark by inverting the watermarked image and re-generating it with an arbitrary prompt, thereby enabling false attribution on malicious content. In this paper, we propose the CSGuard, the first forgery-resistant watermarking schema that leverages compressed sensing to bind the watermarked image generation and verification to a secret matrix. This ensures that only users possessing the secret matrix can correctly embed or verify the image watermark, prevents the illegal users from forgery without compromising generation quality and watermark integrity. Experimental results demonstrate that CSGuard achieves strong forgery resistance, reduces the attack success rate from 100.0\% to 28.12\%, and achieve 100\% detection rate on benign watermarked images without compromising watermarking effectiveness. |
| title | CSGuard: Toward Forgery-Resistant Watermarking in Diffusion Models via Compressed Sensing Constraint |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.01479 |