With the maturity of depth sensors, point clouds have received increasing attention in various applications such as autonomous driving, robotics, surveillance, etc., while deep point cloud learning models have shown to be vulnerable to adversarial attacks. Existing attack methods generally add/delete points or perform point-wise perturbation over point clouds to generate adversarial examples in the data space, which may neglect the geometric characteristics of point clouds. Instead, we propose point cloud attacks from a new perspective -- Graph Spectral Domain Attack (GSDA), aiming to perturb transform coefficients in the graph spectral domain that corresponds to varying certain geometric structure. In particular, we naturally represent a point cloud over a graph, and adaptively transform the coordinates of points into the graph spectral domain via graph Fourier transform (GFT) for compact representation. We then analyze the influence of different spectral bands on the geometric structure of the point cloud, based on which we propose to perturb the GFT coefficients in a learnable manner guided by an energy constraint loss function. Finally, the adversarial point cloud is generated by transforming the perturbed spectral representation back to the data domain via the inverse GFT (IGFT). Experimental results demonstrate the effectiveness of the proposed GSDA in terms of both imperceptibility and attack success rates under a variety of defense strategies. The code is available at https://github.com/WoodwindHu/GSDA.
Perception Matters: Exploring Imperceptible and Transferable Anti-forensics for GAN-generated Fake Face Imagery Detection
University of British Columbia（英属哥伦比亚大学）；Department of Statistics, University of British Columbia（英属哥伦比亚大学统计系）；Department of Electrical and Computer Engineering, University of British Columbia（英属哥伦比亚大学电气与计算机工程系）；School of Information and Communications Engineering, Xi’an Jiaotong University（西安交通大学信息与通信工程学院）
Exploring Adversarial Attacks on Neural Networks: An Explainable Approach
Embry-Riddle Aeronautical University（安柏瑞德航空大学）；University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Tennessee at Martin（田纳西大学马丁分校）；Bowling Green State University（鲍灵格林州立大学）；Purdue University（普渡大学）；Trier University of Applied Sciences（特里尔应用技术大学）
Exploring the Adversarial Robustness of Face Forgery Detection with Decision-based Black-box Attacks
Shanghai Engineering Research Center of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai 200433, China（上海人工智能与机器人工程研究中心，工程与技术学院，复旦大学，上海）；Engineering Research Center of Robotics, Ministry of Education, Academy for Engineering & Technology, Fudan University, Shanghai 200433, China（机器人工程研究中心，教育部，工程与技术学院，复旦大学，上海）；Youtu Lab, Tencent, Shanghai 200000, China（优衣库实验室，腾讯，上海）；vivo Mobile Communication Co., Ltd, Shanghai 200120, China（vivo移动通信有限公司，上海）；Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, China（上海智能信息处理重点实验室，计算机科学学院，复旦大学，上海）
Exploring Structure Consistency for Deep Model Watermarking
University of Science and Technology of China（中国科学技术大学）；Microsoft Cloud AI（微软云人工智能部门）；City University of Hong Kong（香港城市大学）；Wormpex AI Research（Wormpex人工智能研究院）
Exploring Visual Vulnerabilities via Multi-Loss Adversarial Search for Jailbreaking Vision-Language Models
Southeast University（东南大学）；National University of Singapore（新加坡国立大学）；Lancaster University（兰卡斯特大学）；University of California, Los Angeles（加州大学洛杉矶分校）；University of Queensland（昆士兰大学）
Exploring User-level Gradient Inversion with a Diffusion Prior
Vanderbilt University（范德堡大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Mitsubishi Electric Research Laboratories（三菱电机研发实验室）；Vanderbilt University Medical Center（范德堡大学医学中心）
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）
