Pixel Is Not A Barrier: An Effective Evasion Attack for Pixel-Domain Diffusion Models
<|reference_start|>Pixel Is Not A Barrier: An Effective Evasion Attack for Pixel-Domain Diffusion Models: Diffusion Models have emerged as powerful generative models for high-quality image synthesis, with many subsequent image editing techniques based on them. However, the ease of text-based image editing introduces significant risks, such as malicious editing for scams or intellectual property infringement. Previous works have attempted to safeguard images from diffusion-based editing by adding imperceptible perturbations. These methods are costly and specifically target prevalent Latent Diffusion Models (LDMs), while Pixel-domain Diffusion Models (PDMs) remain largely unexplored and robust against such attacks. Our work addresses this gap by proposing a novel attacking framework with a feature representation attack loss that exploits vulnerabilities in denoising UNets and a latent optimization strategy to enhance the naturalness of protected images. Extensive experiments demonstrate the effectiveness of our approach in attacking dominant PDM-based editing methods (e.g., SDEdit) while maintaining reasonable protection fidelity and robustness against common defense methods. Additionally, our framework is extensible to LDMs, achieving comparable performance to existing approaches.<|reference_end|>
@article{shih2024pixel,
    title={Pixel Is Not A Barrier: An Effective Evasion Attack for Pixel-Domain
  Diffusion Models},
    author={Chun-Yen Shih, Li-Xuan Peng, Jia-Wei Liao, Ernie Chu, Cheng-Fu Chou,
  Jun-Cheng Chen},
    journal={arXiv preprint arXiv:2408.11810},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11810},
    primaryClass={cs.CV}
}