4 papers
OmniVL-Guard: Towards Unified Vision-Language Forgery Detection and Grounding via Balanced RL
Jinjie Shen, Jing Wu, Yaxiong Wang +5
Existing forgery detection methods are often limited to uni-modal or bi-modal settings, failing to handle the interleaved text, images, and videos prevalent in real-world misinform…
MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness
Xiaoyun Xu, Shujian Yu, Zhuoran Liu +1
Vision Transformers (ViTs) have emerged as a fundamental architecture and serve as the backbone of modern vision-language models. Despite their impressive performance, ViTs exhibit…
MUNBa: Machine Unlearning via Nash Bargaining
Jing Wu, Mehrtash Harandi
Machine Unlearning (MU) aims to selectively erase harmful behaviors from models while retaining the overall utility of the model. As a multi-task learning problem, MU involves bala…
Erasing Undesirable Influence in Diffusion Models
Jing Wu, Trung Le, Munawar Hayat +1
Diffusion models are highly effective at generating high-quality images but pose risks, such as the unintentional generation of NSFW (not safe for work) content. Although various t…