Diffusion models (DMs) have achieved state-of-the-art performance on various generative
tasks such as image synthesis, text-to-image, and text-guided image-to-image generation …

With the ability to generate high-quality images, text-to-image (T2I) models can be exploited
for creating inappropriate content. To prevent misuse, existing safety measures are either …

The rapid advancement of large models, driven by their exceptional abilities in learning and
generalization through large-scale pre-training, has reshaped the landscape of Artificial …

Text-to-Image (T2I) Diffusion Models (DMs) have garnered widespread attention for their
impressive advancements in image generation. However, their growing popularity has …

Text-to-Image Diffusion Models (T2I DMs) have garnered significant attention for their ability
to generate high-quality images from textual descriptions. However, these models often …

Text-to-image diffusion models (T2I DMs) have achieved excellent performance in image
generation. However, the adversarial robustness of T2I DMs has not been sufficiently …

Diffusion models, while increasingly adept at generating realistic images, are notably
hindered by hallucinations--unrealistic or incorrect features inconsistent with the trained data …

Despite the remarkable success of diffusion models (DMs) in data generation, they exhibit
specific failure cases with unsatisfactory outputs. We focus on one such limitation: the ability …

Recent studies show that text-to-image (T2I) models are vulnerable to adversarial attacks,
especially with noun perturbations in text prompts. In this study, we investigate the impact of …

Visual recognition models are prone to learning spurious correlations induced by a biased
training set where certain conditions B (eg, Indoors) are over-represented in certain classes …