Watermarking LLM-generated text is an important task for tracing its provenance. Existing LLM watermarks preserve provenance under editing, but this same robustness allows an adversary to alter critical content while retaining attribution, a vulnerability known as piggyback spoofing. We introduce an innovative watermar...
Xiao-Yang Feng, Yanjun Zhang, He Zhang et al.· 0 citations
Unauthorized personalization based on diffusion models pose a severe and growing threat to digital privacy by enabling the unauthorized replication and exploitation of individual identities. Existing disrupting-based defenses primarily add invisible perturbations arbitrarily across the entire image space to disrupt the...
Wei-Wei Tan, Rui Wang, Lihua Jing et al.· Proceedings of the Thirty-Fi...· 0 citations
Models trained with reinforcement learning for calibrated decisions (RLCD), such as Jev, answer a typed question about an input, the state, with a probability, a choice, or a score, and software acts on the answer without a person reading it. Their robustness has not been measured: adversarial benchmarks score what a m...
Jian-Yi Hu, Hang Zhang, Yi Liu et al.· 0 citations
ODPure is proposed, a novel input-stage black-box defense for object detection, which is based on input purification that ensures stable perception flows and provides robust defense against diverse backdoor attacks and trigger types while preserving baseline accuracy.
Li Zeng, Ming-Cheng Duan, Long-Fei Fan et al.· 0 citations
The results show that reliable malicious-Skill detection requires both broader cross-source benchmark coverage and evaluation that jointly measures attack detection and benign over-flagging.
Gradient-seeded Reinforcement Learning And Stealthy Pruning (GRASP), a three-stage framework that tackles challenges of adversarial attacks on machine learning-based malware detectors, and out-performs baselines, achieving higher attack success with fewer queries and smaller file-size inflation.
Yu-Tong Liu, Jian-Ting Ning, Qi Feng et al.· Proceedings of the Thirty-Fi...· 1 citation
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