arXiv Computer Vision
Region-Level Black-Box Defense Against Stealthy Embedding-Space Backdoors in CLIP
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Beyond Small Patches: Black-Box Detection and Purification of Diverse Backdoor Triggers
The paper introduces TRIM, a black‑box defense for backdoor attacks in computer vision models. TRIM identifies and removes malicious trigger regions at inference time using region‑based segmentation, adaptive trigger discovery via inpainting and diffusion, and selective purification, without needing model internals, training data, or clean samples. Experiments on various datasets and trigger types show TRIM reduces attack success rates to as low as 1.16% while maintaining high clean accuracy.
arXiv AI
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Robustness of Vision Language Models Against Split-Image Harmful Input Attacks
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Breaking the Stealth-Potency Trade-off in Clean-Image Backdoors with Generative Trigger Optimization
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arXiv Machine Learning
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BYORn: Bootstrap Your Own Responses to Defend Large Vision-Language Models Against Backdoor Attacks
arXiv:2606. 02947v1 Announce Type: new Abstract: Supervised fine-tuning is the predominant approach for adapting autoregressive vision-language models to downstream tasks.