In the domain of backdoor attacks, accurate labeling of injected data is essential for evading rudimentary detection mechanisms. This imperative has catalyzed the development of clean-label attacks, which are notably more elusive as they preserve the original labels of the injected data. Current clean-label attack methodologies primarily depend on extensive knowledge of the training dataset. However, practically, such comprehensive dataset access is often unattainable, given that training datasets are typically compiled from various independent sources. Departing from conventional clean-label attack methodologies, our research introduces DFB, a data-free, low-budget, and high-efficacy clean-label backdoor Attack. DFB is unique in its independence from training data access, requiring solely the knowledge of a specific target class. Tested on CIFAR10, Tiny-ImageNet, and TSRD, DFB demonstrates remarkable efficacy with minimal poisoning rates of just 0.1%, 0.025%, and 0.4%, respectively. These rates are significantly lower than those required by existing methods such as LC, HTBA, BadNets, and Blend, yet DFB achieves superior attack success rates. Furthermore, our findings reveal that DFB poses a formidable challenge to four established backdoor defense algorithms, indicating its potential as a robust tool in advanced clean-label attack strategies.
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）；Shenzhen Research Institute of Northwestern Polytechnical University（西北工业大学深圳研究院）；School of Software, Northwestern Polytechnical University（西北工业大学软件学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；School of Cybersecurity, Northwestern Polytechnical University（西北工业大学网络安全学院）
Temporal Poisoning: Clean-Label Backdoors via Event Redistribution in SNNs
Radboud University（拉德堡德大学）；Ikerlan Research Centre（伊克尔兰研究中心）；University of Bergen（卑尔根大学）；University of Zagreb（萨格勒布大学）；Faculty of Electrical Engineering and Computing（电气工程与计算机学院）
SSCL-BW: Sample-Specific Clean-Label Backdoor Watermarking for Dataset Ownership Verification
School of Control and Computer Engineering（控制与计算机工程学院）；North China Electric Power University（华北电力大学）；Research Institute（研究院）；China Unicom（中国联合网络通信集团）；Technical Department（技术部）
The Pennsylvania State University（宾夕法尼亚州立大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；University of Georgia（佐治亚大学）；University of California Berkeley（加州大学伯克利分校）
FreeEagle: Detecting Complex Neural Trojans in Data-Free Cases
Zhejiang University（浙江大学）；Pennsylvania State University（宾夕法尼亚州立大学）；Chinese Aeronautical Establishment（中国航空研究院）；National University of Defense Technology（国防科技大学）
Triggerless Backdoor Attack for NLP Tasks with Clean Labels
Zhejiang University（浙江大学）；Nanyang Technological University（南洋理工大学）；Shanghai Institute for Advanced Study of Zhejiang University（浙江大学上海高等研究院）；Shanghai AI Laboratory（上海人工智能实验室）；Chongqing University（重庆大学）；Peng Cheng Laboratory（鹏城实验室）；Peking University（北京大学）
CBPF: Filtering Poisoned Data Based on Composite Backdoor Attack
School of Computer Science and Technology, Zhejiang Gongshang University（浙江工商大学计算机科学与技术学院）；School of Mathematical and Computational Sciences, Massey University（梅西大学数学与计算科学学院）
Defusing the Trigger: Tail-Risk-Informed Attention Rebalancing for LLM Backdoor Mitigation
School of Cyber Science and Technology（网络安全科学与技术学院）；Harbin Institute of Technology（哈尔滨工业大学）；Peng Cheng Laboratory（鹏城实验室）；University of Luxembourg（卢森堡大学）
Ouroboros: Self-Referential Backdoor Attacks on Speech Enhancement via Clean Audio Triggers
Faculty of Electrical Engineering and Computer Science, Ningbo University（宁波大学电气工程与计算机科学学院）；College of Artificial Intelligence, Ningbo University of Finance and Economics（宁波财经学院人工智能学院）
Unveiling and Mitigating Backdoor Vulnerabilities based on Unlearning Weight Changes and Backdoor Activeness
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Backdoor Secrets Unveiled: Identifying Backdoor Data with Optimized Scaled Prediction Consistency
University of Alberta（阿尔伯塔大学）；Michigan State University（密歇根州立大学）；Illinois Institute of Technology（伊利诺伊理工学院）；DSO National Laboratories（新加坡国防科技局国家实验室）；MIT-IBM Watson AI Lab, IBM Research（IBM研究院MIT-IBM沃森人工智能实验室）
Mutual Information Guided Backdoor Mitigation for Pre-trained Encoders
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）
Generalization Bound and New Algorithm for Clean-Label Backdoor Attack
Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Kaiyuan International Mathematical Sciences Institute（开源国际数学科学研究所）
TransCAB: Transferable Clean-Annotation Backdoor to Object Detection with Natural Trigger in Real-World
The University of Adelaide（阿德莱德大学）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织Data61）；Nanjing University of Science and Technology（南京理工大学）；The University of Western Australia（西澳大学）
