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DeceptionBench: A Comprehensive Benchmark for AI Deception Behaviors in Real-world Scenarios
Yao Huang, Yitong Sun, Yichi Zhang +3
Despite the remarkable advances of Large Language Models (LLMs) across diverse cognitive tasks, the rapid enhancement of these capabilities also introduces emergent deceptive behav…
NDM: A Noise-driven Detection and Mitigation Framework against Implicit Sexual Intentions in Text-to-Image Generation
Yitong Sun, Yao Huang, Ruochen Zhang +4
Despite the impressive generative capabilities of text-to-image (T2I) diffusion models, they remain vulnerable to generating inappropriate content, especially when confronted with…
Never compromise with vulnerabilities: a comprehensive survey on AI governance
Yuchu Jiang, Jian Zhao, Yuchen Yuan +62
The rapid advancement of AI has expanded its capabilities across domains, yet introduced critical technical vulnerabilities, such as algorithmic bias and adversarial sensitivity, t…
The Path to Reconciling Quality and Safety in Text-to-Image Generation: Dataset, Method, and Evaluation
Shouwei Ruan, Zhenyu Wu, Yao Huang +5
Content safety is a fundamental challenge for text-to-image (T2I) models, yet prevailing methods enforce a debilitating trade-off between safety and generation quality. We argue th…