Reverse Engineering Imperceptible Backdoor Attacks on Deep Neural Networks for Detection and Training Set Cleansing (2010.07489v1)

Abstract: Backdoor data poisoning is an emerging form of adversarial attack usually against deep neural network image classifiers. The attacker poisons the training set with a relatively small set of images from one (or several) source class(es), embedded with a backdoor pattern and labeled to a target class. For a successful attack, during operation, the trained classifier will: 1) misclassify a test image from the source class(es) to the target class whenever the same backdoor pattern is present; 2) maintain a high classification accuracy for backdoor-free test images. In this paper, we make a break-through in defending backdoor attacks with imperceptible backdoor patterns (e.g. watermarks) before/during the training phase. This is a challenging problem because it is a priori unknown which subset (if any) of the training set has been poisoned. We propose an optimization-based reverse-engineering defense, that jointly: 1) detects whether the training set is poisoned; 2) if so, identifies the target class and the training images with the backdoor pattern embedded; and 3) additionally, reversely engineers an estimate of the backdoor pattern used by the attacker. In benchmark experiments on CIFAR-10, for a large variety of attacks, our defense achieves a new state-of-the-art by reducing the attack success rate to no more than 4.9% after removing detected suspicious training images.