This paper proposes a novel text-based perturbation method, Natural Textual Unlearnable Examples (NTUE), which can effectively mislead LVLMs by subtly manipulating the image label, establishing a shortcut between an identity of interest and a separate target, without introducing factual misinformation or affecting read...
Chen-Yu Yi, Jia-Wen Zong, Sami Hormi et al.· 0 citations
World models and video understanding models are increasingly deployed in dynamic visual environments, yet their robustness to physical adversarial threats remains largely underexplored. We study whether a single physical adversarial texture can mislead the victim model across different real-world scenarios where observ...
Song Xia, Mei-Wen Ding, Chen-Qi Kong et al.· 0 citations
Vision-Language-Action (VLA) policies are vulnerable to localized physical perturbations, yet existing certified patch defenses target discrete labels and cannot directly certify continuous, temporally correlated actions. We introduce CertVLA, a certified defense for closed-loop VLA control under bounded patch and text...
Hui Lu, Zhi-Jie Peng, Yuqi Lin et al.· 0 citations
