T2VSafetyBench: Evaluating the Safety of Text-to-Video Generative Models
UCAS（中国科学院大学）；Tsinghua University（清华大学）；Institute for AI（人工智能研究院）；Tsinghua-Bosch Joint ML Center（清华-博世联合机器学习中心）；THBI Lab（THBI实验室）；BNRist Center（BNRist中心）；RealAI
The recent development of Sora leads to a new era in text-to-video (T2V) generation. Along with this comes the rising concern about its security risks. The generated videos may contain illegal or unethical content, and there is a lack of comprehensive quantitative understanding of their safety, posing a challenge to their reliability and practical deployment. Previous evaluations primarily focus on the quality of video generation. While some evaluations of text-to-image models have considered safety, they cover fewer aspects and do not address the unique temporal risk inherent in video generation. To bridge this research gap, we introduce T2VSafetyBench, a new benchmark designed for conducting safety-critical assessments of text-to-video models. We define 12 critical aspects of video generation safety and construct a malicious prompt dataset including real-world prompts, LLM-generated prompts and jailbreak attack-based prompts. Based on our evaluation results, we draw several important findings, including: 1) no single model excels in all aspects, with different models showing various strengths; 2) the correlation between GPT-4 assessments and manual reviews is generally high; 3) there is a trade-off between the usability and safety of text-to-video generative models. This indicates that as the field of video generation rapidly advances, safety risks are set to surge, highlighting the urgency of prioritizing video safety. We hope that T2VSafetyBench can provide insights for better understanding the safety of video generation in the era of generative AI.
Qorgau: Evaluating LLM Safety in Kazakh-Russian Bilingual Contexts
Mohamed bin Zayed University of Artificial Intelligence（莫扎伊德大学人工智能学院）；Monash University（莫纳什大学）；University of Copenhagen（哥本哈根大学）；The University of Melbourne（墨尔本大学）；LibrAI（LibrAI公司）；Institute of Foundation Models（基础模型研究所）
DSBench: A Comprehensive Benchmark for Evaluating External and In-Cabin Risks
University of Science and Technology of China（中国科学技术大学）；Georgia Institute of Technology（佐治亚理工学院）；Xiaomi EV（小米电动车）；Fudan University（复旦大学）；Xidian University（西安电子科技大学）；South China University of Technology（华南理工大学）；The University of Hong Kong（香港大学）
FinSafetyBench: Evaluating LLM Safety in Real-World Financial Scenarios
Shanghai University of Finance and Economics（上海财经大学）；Beihang University（北航）；Southern University of Science and Technology（南方科技大学）；MoE Key Laboratory of Interdisciplinary Research of Computation and Economics（计算与经济学交叉学科研究重点实验室）
Deep Generative Multi-Agent Imitation Model as a Computational Benchmark for Evaluating Human Performance in Complex Interactive Tasks: A Case Study in Football
Fréchet Power-Scenario Distance: A Metric for Evaluating Generative AI Models across Multiple Time-Scales in Smart Grids
Department of Electrical and Computer Engineering, Texas A&M University（电气与计算机工程系，德克萨斯农工大学）；John A. Paulson School of Engineering and Applied Sciences, Harvard University（约翰·A·保罗森工程与应用科学学院，哈佛大学）
