Generating adversarial scenarios, which have the potential to fail autonomous driving systems, provides an effective way to improve robustness. Extending purely data-driven generative models, recent specialized models satisfy additional controllable requirements such as embedding a traffic sign in a driving scene by manipulating patterns implicitly in the neuron level. In this paper, we introduce a method to incorporate domain knowledge explicitly in the generation process to achieve the Semantically Adversarial Generation (SAG). To be consistent with the composition of driving scenes, we first categorize the knowledge into two types, the property of objects and the relationship among objects. We then propose a tree-structured variational auto-encoder (T-VAE) to learn hierarchical scene representation. By imposing semantic rules on the properties of nodes and edges in the tree structure, explicit knowledge integration enables controllable generation. We construct a synthetic example to illustrate the controllability and explainability of our method in a succinct setting. We further extend to realistic environments for autonomous vehicles: our method efficiently identifies adversarial driving scenes against different state-of-the-art 3D point cloud segmentation models and satisfies the traffic rules specified as the explicit knowledge.
Adaptive Guidance Semantically Enhanced via Multimodal LLM for Edge-Cloud Object Detection
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Institute of AI for Industries（工业人工智能研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Towards a 3D Transfer-based Black-box Attack via Critical Feature Guidance
Nanjing University of Science and Technology（南京理工大学）；STCA, Microsoft（STCA微软公司）；Nanyang Technological University（南洋理工大学）；Nanjing University of Industry Technology（南京工业技术大学）
Semantically Guided Adversarial Testing of Vision Models Using Language Models
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences（波兰科学院理论与应用信息学研究所）；University of Lille, CNRS, Inria, Centrale Lille, UMR 9189 CRIStAL（里尔大学）
Adversarial Guided Diffusion Models for Adversarial Purification
Tokyo University of Agriculture and Technology（东京农工大学）；RIKEN Center for Advanced Intelligence Project (RIKEN AIP)（理化学研究所先进智能项目中心）；Hangzhou Dianzi University（杭州电子科技大学）
Semantically Decomposing the Latent Spaces of Generative Adversarial Networks
University of California, San Diego（加利福尼亚大学圣地亚哥分校）；Carnegie Mellon University（卡内基梅隆大学）；Amazon AI（亚马逊人工智能）；Stanford University（斯坦福大学）；University of California San Diego（加利福尼亚大学圣地亚哥分校）
Guidance Through Surrogate: Towards a Generic Diagnostic Attack
Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Australian National University（澳大利亚国立大学）；Qualcomm（高通公司）；Linköping University（林雪平大学）
Retrieval-Augmented Anatomical Guidance for Text-to-CT Generation
Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy（人工智能与计算机系统单位，工程系，罗马生物医学大学，意大利罗马）；Department of Diagnostics and Intervention, Biomedical Engineering and Radiation Physics, Umeå University, Umeå, Sweden（诊断与介入系，生物医学工程与放射物理，乌梅大学，瑞典乌梅）
Improving Fast Adversarial Training via Self-Knowledge Guidance
School of Cyber Science and Engineering, Southeast University（东南大学网络空间科学与工程学院）；City University of Hong Kong（香港城市大学）；Purple Mountain Laboratories（紫金山实验室）；School of Information Science and Engineering, Ocean University of China（中国海洋大学信息科学与工程学院）；University of Macau（澳门大学）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）
Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；Monash University（莫纳什大学）；Temple University（天普大学）；Peng Cheng Laboratory（鹏城实验室）
Not all tokens contribute equally to diffusion learning
School of Mechanical Engineering, Guizhou University, Guizhou, China（贵州大学机械工程学院）；School of Information Science and Technology, Hangzhou Normal University, Zhejiang, China（杭州师范大学信息科学与技术学院）
Exploring Semantic-constrained Adversarial Example with Instruction Uncertainty Reduction
State Key Laboratory of Complex & Critical Software Environment (CCSE), Beihang University（复杂与关键软件环境国家重点实验室，北京航空航天大学）；Zhongguancun Laboratory（中关村实验室）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Department of Information Technology and Electrical Engineering, ETH Zurich（苏黎世联邦理工学院信息科技与电气工程系）
Adversarially Guided Diffusion for LiDAR Range Image Synthesis
School of Electrical and Computer Engineering, National Technical University of Athens（雅典国立技术大学电气与计算机工程学院）；Industrial Systems Institute, Athena Research Center（雅典娜研究中心工业系统研究所）
Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition
Institute of High Performance Computing, Agency for Science, Technology, and Research（高性能计算研究所，科技研究局）；Centre for Artificial Intelligence, University of Technology Sydney（悉尼科技大学人工智能中心）；Pattern Recognition and Intelligent System Laboratory, Beijing University of Posts and Telecommunications（北京邮电大学模式识别与智能系统实验室）；Brookhaven National Laboratory（布鲁克海文国家实验室）；InnoPeak Technology Inc.；Key Laboratory of Medical Molecular Virology, School of Basic Medical Sciences, Fudan University（复旦大学基础医学院医学分子病毒学重点实验室）
