Explore the Effect of Data Selection on Poison Efficiency in Backdoor Attacks

Deep Neural Networks (DNNs) have achieved remarkable success across a wide range of tasks; however, their susceptibility to backdoor attacks remains a significant concern. Existing methods predominantly focus on optimizing the construction phase of backdoor attacks, aiming to reduce the detectability of trigger patterns and enhance stealth. In contrast, the selection phase—specifically the identification of appropriate benign samples for poisoning—has received limited attention. Recent studies have explored efficient poisoning sample selection to improve attack stealth. However, the underlying factors that determine the informativeness or effectiveness of a sample for backdoor learning remain poorly understood. To address this gap, we investigate the role of forgettable event and loss landscape curvature in enhancing poisoning sample efficiency. Our findings reveal that samples most likely to be forgotten during the poisoning process are crucial for effective attacks, and that low-curvature regions of the loss surface correlate with higher poisoning efficiency. Based on these insights, we introduce the Improved Filtering and Updating Strategy (FUS++), which significantly outperforms traditional selection methods in terms of efficiency. Our contributions provide new perspectives on sample selection for backdoor attacks and propose a novel strategy to improve poisoning efficacy.