Minimalism is King! High-Frequency Energy-Based Screening for Data-Efficient Backdoor Attacks

Given the effectiveness of deep neural networks in various fields, the security of neural networks has received great attention. The backdoor attack, which induces malicious behaviors of models by poisoning part of the training set, still remains a challenging problem. Many recent efforts have proposed different ways of embedding backdoors to improve the stealthiness of backdoor attacks. Yet, lowering the percentage of poisoned samples is one of the most direct ways to increase stealthiness. A recent study (Filtering-and-Updating strategy, FUS) has revealed that the sample selection for poisoning is also crucial, as different samples contribute differently to the final decision boundary of the network. Concretely, they utilize each sample’s forgetting events during the training stage to identify which samples will contribute more to the network’s prediction. The training phase of their search method, however, is computationally expensive and slow. To overcome this, in this paper, we propose an efficient sample selection strategy based on the high-frequency energy (HFE) of training samples with a global screening and updating strategy, which can not only achieve a higher backdoor-attack success rate but also reduce the searching time by a factor of 4320 compared to FUS (12 hours vs 10 seconds). The extensive experiment results on CIFAR-10, CIFAR-100, and ImageNet-10 have shown that our proposed method is much simpler, faster, and more efficient.