Supervised fine-tuning can substantially improve the downstream utility of large language models (LLMs) but may compromise their safety. Existing safety-preserving methods constrain downstream updates using safety-related parameters or subspaces, but mainly focus on safety preservation rather than joint safety and util...
Wei-Wei Qi, Chong-Yu Wang, Tian-Hang Zheng et al.· 0 citations
Advances in speech synthesis have made deepfake speeches increasingly convincing, posing growing threats to security. While self-supervised learning (SSL) based detectors achieve state-of-the-art performance, their computational demands (typically 300M+ parameters) prevent deployment on resource-constrained devices. Ex...
Miao He, Peng Cheng, Zhong-Jie Ba et al.· 0 citations
By suppressing semantic interference, this paper visualize, for the first time, semantics-irrelevant texture patterns across generation paradigms and proposes DTS-Det, a detection framework that captures and leverages such relations for generalized AI-generated image detection.
Haoyu Wang, Yiming Qin, Zhongjie Ba et al.· arXiv.org· 0 citations
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