Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

It has been recognized that the data generated by the denoising diffusion probabilistic
model (DDPM) improves adversarial training. After two years of rapid development in …

Existing image restoration methods mostly leverage the posterior distribution of natural
images. However, they often assume known degradation and also require supervised …

Despite the record-breaking performance in Text-to-Image (T2I) generation by Stable
Diffusion, less research attention is paid to its adversarial robustness. In this work, we study …

Neural networks are known to be susceptible to adversarial samples: small variations of
natural examples crafted to deliberatelymislead the models. While they can be easily …

Diffusion frameworks have achieved comparable performance with previous state-of-the-art
image generation models. This paper proposes DiffusionInst, a novel framework …

We question the current evaluation practice on diffusion-based purification methods.
Diffusion-based purification methods aim to remove adversarial effects from an input data …

Diffusion-based purification defenses leverage diffusion models to remove crafted
perturbations of adversarial examples and achieve state-of-the-art robustness. Recent …

Diffusion models have been leveraged to perform adversarial purification and thus provide
both empirical and certified robustness for a standard model. On the other hand, different …

Safeguarding data from unauthorized exploitation is vital for privacy and security, especially
in recent rampant research in security breach such as adversarial/membership attacks. To …