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This study presents the first comprehensive safety evaluation of the DeepSeek models, focusing on evaluating the safety risks associated with their generated content.
Perceptual-sensitive gan for generating adversarial patches
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Patrick Schramowski, Manuel Brack, Björn Deiseroth, and Kristian Kersting · 2023
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Universal and transferable adversarial attacks on aligned language models, 2023
Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J. Zico Kolter, and Matt Fredrikson · 2023
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Haozhe An, Christabel Acquaye, Colin Wang, Zongxia Li, and Rachel Rudinger · 2024
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Yingkai Dong, Zheng Li, Xiangtao Meng, Ning Yu, and Shanqing Guo · 2024
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How does deepseek-r1 perform on usmle?
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
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Cogmorph: Cognitive morphing attacks for text-to-image models, 2025
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Deepseek-vl2: Mixture-of-experts vision-language models for advanced multimodal understanding
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Performance of deepseek-r1 in ophthalmology: An evaluation of clinical decision-making and cost-effectiveness
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The hidden risks of large reasoning models: A safety assessment of r1
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