EVADE-Bench: Multimodal Benchmark for Evaluating and Enhancing Evasive Content Detection 事件

EVADE-Bench: Multimodal Benchmark for Evaluating and Enhancing Evasive Content Detection arXiv:2505.17654v4 Announce Type: replace Abstract: E-commerce platforms increasingly rely on Large Language Models (LLMs) and Vision Language Models (VLMs) to detect illicit or misleading product content. However, these models remain vulnerable to evasive content, which refers to inputs that have been deliberately modified through techniques such as word splitting, euphemistic language, or image cropping t