BadRSSD: Backdoor Attacks on Regularized Self-Supervised Diffusion Models
Self-supervised diffusion models learn high-quality visual representations via latent space denoising. However, their representation layer poses a distinct threat: unlike traditional attacks targeting generative outputs, its unconstrained latent semantic space allows for stealthy backdoors, permitting malicious control upon triggering. In this paper, we propose BadRSSD, the first backdoor attack targeting the representation layer of self-supervised diffusion models. Specifically, it hijacks the semantic representations of poisoned samples with triggers in Principal Component Analysis (PCA) space toward those of a target image, then controls the denoising trajectory during diffusion by app...
| Authors | Jiayao Wang, Yiping Zhang, Mohammad Maruf Hasan et al. |
|---|---|
| Primary category | cs.CR |
| Published | 1 March 2026 |
| Last updated at source | 1 March 2026 |
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