An Imperceptible Data Augmentation Based Blackbox Clean-Label Backdoor Attack on Deep Neural Networks

Deep neural networks (DNNs) have permeated into many diverse application domains, making them attractive targets of malicious attacks. DNNs are particularly susceptible to data poisoning attacks. Such attacks can be made more venomous and harder to detect by poisoning the training samples without changing their ground-truth labels. Despite its pragmatism, the clean-label requirement imposes a stiff restriction and strong conflict in simultaneous optimization of attack stealth, success rate, and utility of the poisoned model. Attempts to circumvent the pitfalls often lead to a high injection rate, ineffective embedded backdoors, unnatural triggers, low transferability, and/or poor robustness. In this paper, we overcome these constraints by amalgamating different data augmentation techniques for the backdoor trigger. The spatial intensities of the augmentation methods are iteratively adjusted by interpolating the clean sample and its augmented version according to their tolerance to perceptual loss and augmented feature saliency to target class activation. Our proposed attack is comprehensively evaluated on different network models and datasets. Compared with state-of-the-art clean-label backdoor attacks, it has lower injection rate, stealthier poisoned samples, higher attack success rate, and greater backdoor mitigation resistance while preserving high benign accuracy. Similar attack success rates are also demonstrated on the Intel Neural Compute Stick 2 edge AI device implementation of the poisoned model after weight-pruning and quantization.