Artificial Intelligence Security Competition (AISC)
Yinpeng Dong, Peng Chen, Senyou Deng, Lianji L, Yi Sun, Hanyu Zhao,, Jiaxing Li, Yunteng Tan, Xinyu Liu, Yangyi Dong, Enhui Xu, Jincai Xu, Shu Xu,, Xuelin Fu, Changfeng Sun, Haoliang Han, Xuchong Zhang, Shen Chen, Zhimin Sun,, Junyi Cao, Taiping Yao, Shouhong Ding, Yu Wu

TL;DR
The paper introduces the Artificial Intelligence Security Competition (AISC), which aims to advance AI security research through three tracks focused on deepfake, autonomous driving, and face recognition security challenges.
Contribution
It presents the competition structure, rules, and top solutions, fostering progress in AI security by encouraging innovative approaches across multiple application domains.
Findings
Top solutions demonstrate effective defense strategies against AI security threats.
The competition promotes collaboration and innovation in AI security research.
Results highlight key vulnerabilities and mitigation techniques in AI systems.
Abstract
The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems. To accelerate the research on AI security, the Artificial Intelligence Security Competition (AISC) was organized by the Zhongguancun Laboratory, China Industrial Control Systems Cyber Emergency Response Team, Institute for Artificial Intelligence, Tsinghua University, and RealAI as part of the Zhongguancun International Frontier Technology Innovation Competition (https://www.zgc-aisc.com/en). The competition consists of three tracks, including Deepfake Security Competition, Autonomous Driving Security Competition, and Face Recognition Security Competition. This report will introduce the competition rules of these three tracks and the solutions of top-ranking teams in each track.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsLegal and Policy Issues · Adversarial Robustness in Machine Learning
