The transferability and robustness of adversarial examples are two practical yet important properties for black-box adversarial attacks. In this paper, we explore effective mechanisms to boost both of them from the perspective of network hierarchy, where a typical network can be hierarchically divided into output stage, intermediate stage and input stage. Since over-specialization of source model, we can hardly improve the transferability and robustness of the adversarial perturbations in the output stage. Therefore, we focus on the intermediate and input stages in this paper and propose a transferable and robust adversarial perturbation generation (TRAP) method. Specifically, we propose the dynamically guided mechanism to continuously calculate accurate directional guidances for perturbation generation in the intermediate stage. In the input stage, instead of the single-form transformation augmentations adopted in the existing methods, we leverage multiform affine transformation augmentations to further enrich the input diversity and boost the robustness and transferability of the adversarial perturbations. Extensive experiments demonstrate that our TRAP achieves impressive transferability and high robustness against certain interferences.
Cross-Modal Transferable Image-to-Video Attack on Video Quality Metrics
Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）；ISP RAS Research Center for Trusted Artificial Intelligence（俄罗斯科学院信息与系统研究所在可信人工智能研究中心）；MSU Institute for Artificial Intelligence（莫斯科大学人工智能研究所）；Laboratory of Innovative Technologies for Processing Video Content（视频内容处理创新技术实验室）
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 Simple and Transferable Recognition-Aware Image Processing
University of California, Berkeley（加州大学伯克利分校）；Carnegie Mellon University（卡内基梅隆大学）；National University of Singapore（新加坡国立大学）；DeepMind（深度思维）；Adobe Research（奥多比研究院）
Exploring Adversarial Robustness and Safety Alignment in Multilingual Multi-Modal Large Language Models
Mohamed Bin Zayed University of AI, UAE（穆罕默德·本·扎耶德人工智能大学，阿联酋）；Khalifa University, UAE（卡比拉大学，阿联酋）；Australian National University, Australia（澳大利亚国立大学，澳大利亚）
HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification
Jalpaiguri Government Engineering College（贾尔派古里政府工程学院）；Indian Institute of Information Technology, Sri City（斯里城印度信息技术学院）；Indian Statistical Institute（印度统计研究所）
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model
The University of Sydney（悉尼大学）；University of Malaya（马来亚大学）；Suzhou Yierqi（苏州意尔奇）；The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；University of Wollongong（伍伦贡大学）
SGHA-Attack: Semantic-Guided Hierarchical Alignment for Transferable Targeted Attacks on Vision-Language Models
Guangdong Province Key Lab of Information Security Technology, and School of Computer Science and Engineering, Sun Yat-sen University（广东信息安全技术重点实验室，计算机科学与工程学院，中山大学）；Nanyang Technological University（南洋理工大学）；School of Cyber Science and Technology, Shenzhen Campus, Sun Yat-sen University（网络安全科学与技术学院，深圳校区，中山大学）
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）
Semantic Routing: Exploring Multi-Layer LLM Feature Weighting for Diffusion Transformers
Peking University（北京大学）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Nanjing University（南京大学）；Fudan University（复旦大学）
