AI RESEARCH
BackdoorIDS: Zero-shot Backdoor Detection for Pretrained Vision Encoder
arXiv CS.CV
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ArXi:2603.11664v1 Announce Type: new Self-supervised and multimodal vision encoders learn strong visual representations that are widely adopted in downstream vision tasks and large vision-language models (LVLMs). However, downstream users often rely on third-party pretrained encoders with uncertain provenance, exposing them to backdoor attacks. In this work, we propose BackdoorIDS, a simple yet effective zero-shot, inference-time backdoor samples detection method for pretrained vision encoders. BackdoorIDS is motivated by two observations: Attention Hijacking and Restoration.