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Exposing and Defending the Achilles' Heel of Video Mixture-of-Experts
Songping Wang, Qinglong Liu, Yueming Lyu +3
Mixture-of-Experts (MoE) has demonstrated strong performance in video understanding tasks, yet its adversarial robustness remains underexplored. Existing attack methods often treat…
RunawayEvil: Jailbreaking the Image-to-Video Generative Models
Songping Wang, Rufan Qian, Yueming Lyu +5
Image-to-Video (I2V) generation synthesizes dynamic visual content from image and text inputs, providing significant creative control. However, the security of such multimodal syst…
GOOD: Training-Free Guided Diffusion Sampling for Out-of-Distribution Detection
Xin Gao, Jiyao Liu, Guanghao Li +8
Recent advancements have explored text-to-image diffusion models for synthesizing out-of-distribution (OOD) samples, substantially enhancing the performance of OOD detection. Howev…
An Effective End-to-End Solution for Multimodal Action Recognition
Songping Wang, Xiantao Hu, Yueming Lyu +1
Recently, multimodal tasks have strongly advanced the field of action recognition with their rich multimodal information. However, due to the scarcity of tri-modal data, research o…
Fast Adversarial Training with Weak-to-Strong Spatial-Temporal Consistency in the Frequency Domain on Videos
Songping Wang, Hanqing Liu, Yueming Lyu +5
Adversarial Training (AT) has been shown to significantly enhance adversarial robustness via a min-max optimization approach. However, its effectiveness in video recognition tasks…
Anti-Aesthetics: Protecting Facial Privacy against Customized Text-to-Image Synthesis
Songping Wang, Yueming Lyu, Shiqi Liu +4
The rise of customized diffusion models has spurred a boom in personalized visual content creation, but also poses risks of malicious misuse, severely threatening personal privacy…