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This work proposes a transferable graph purification scheme, named ProGAP, to bridge adversarial defense knowledge via vulnerability-aware graph prompt learning, and achieves 1%-9% improvement, and reduces the time consumption by up to 2.2x.
ColluSkill is proposed, a collusive multi-skill-chain attack framework that decomposes a complete malicious intent into interdependent sub-payloads embedded in independently packaged skills and employs LLM-based chain planning and scanner-feedback refinement to preserve chain-level attack semantics.
Inspired by Bayesian posterior inference, this work reformulate backdoor detection as a representation-conditioned image likelihood estimation problem parameterized by a conditional diffusion generative model, and fine-tune a pretrained diffusion model, leveraging its generative prior to map data onto the natural image...
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