Backdoor attacks, representing an emerging threat to the integrity of deep neural networks, have garnered significant attention due to their ability to compromise deep learning systems clandestinely. While numerous backdoor attacks occur within the digital realm, their practical implementation in real-world prediction systems remains limited and vulnerable to disturbances in the physical world. Consequently, this limitation has given rise to the development of physical backdoor attacks, where trigger objects manifest as physical entities within the real world. However, creating the requisite dataset to train or evaluate a physical backdoor model is a daunting task, limiting the backdoor researchers and practitioners from studying such physical attack scenarios. This paper unleashes a recipe that empowers backdoor researchers to effortlessly create a malicious, physical backdoor dataset based on advances in generative modeling. Particularly, this recipe involves 3 automatic modules: suggesting the suitable physical triggers, generating the poisoned candidate samples (either by synthesizing new samples or editing existing clean samples), and finally refining for the most plausible ones. As such, it effectively mitigates the perceived complexity associated with creating a physical backdoor dataset, transforming it from a daunting task into an attainable objective. Extensive experiment results show that datasets created by our "recipe" enable adversaries to achieve an impressive attack success rate on real physical world data and exhibit similar properties compared to previous physical backdoor attack studies. This paper offers researchers a valuable toolkit for studies of physical backdoors, all within the confines of their laboratories.
Backdoor Samples Detection Based on Perturbation Discrepancy Consistency in Pre-trained Language Models
Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University（航天信息安全部门、教育部、网络安全科学与工程学院、武汉大学）
Gungnir: Exploiting Stylistic Features in Images for Backdoor Attacks on Diffusion Models
School of Economics and Management（经济管理学院）；East China Normal University（华东师范大学）；School of Information Science and Technology（信息科学与技术学院）；Shanghai Tech University（上海理工大学）；Shanghai Development Center of Computer Software Technology（上海计算机软件技术开发中心）；School of Computer and Information Engineering（计算机与信息工程学院）；Shanghai Polytechnic University（上海理工大学）
Versatile Backdoor Attack with Visible, Semantic, Sample-Specific, and Compatible Triggers
School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Analyzing and Mitigating Poisoning Attacks on Flow-Based Intrusion Detection
CortAIx Labs, Thales Deutschland（Thales Deutschland）；Grey Solutions；University of Osnabrück（奥斯纳布吕克大学）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；Ulm University（乌尔姆大学）
