ASet of Generalized Components to Achieve Effective Poison-only Clean-label Backdoor Attacks with Collaborative Sample Selection and Triggers

Poison-only Clean-label Backdoor Attacks (PCBAs) aim to covertly inject attackerdesired behavior into DNNs by merely poisoning the dataset without changing the labels. To effectively implant a backdoor, multiple triggers are proposed for various attack requirements of Attack Success Rate (ASR) and stealthiness. Additionally, sample selection enhances clean-label backdoor attacks' ASR by meticulously selecting "hard" samples instead of random samples to poison. Current methods, however, 1) usually handle the sample selection and triggers in isolation, leading to limited performance on both ASR and stealthiness when converted to PCBAs. Therefore, we seek to explore the bi-directional collaborative relations between the sample selection and triggers to address the above dilemma.