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cs.CV2026
When the Editing Intent Is Split: A Cross-Modal Jailbreak Attack for Large Image Editing Models
Xianmin Ye, Zhiyuan Fan, Tianju Liu +3
Large image editing models increasingly infer editing intent from both textual and visual inputs, creating an underexplored safety risk when harmful intent is distributed across mo…
cs.CV2026
Robust MLLM Unlearning via Visual Knowledge Distillation
Yuhang Wang, Zhenxing Niu, Haoxuan Ji +3
Recently, machine unlearning approaches have been proposed to remove sensitive information from well-trained large models. However, most existing methods are tailored for LLMs, whi…
cs.CV2024
Interpreting and Mitigating Hallucination in MLLMs through Multi-agent Debate
Zheng Lin, Zhenxing Niu, Zhibin Wang +1
MLLMs often generate outputs that are inconsistent with the visual content, a challenge known as hallucination. Previous methods focus on determining whether a generated output is…