Invisible Poisoning: Highly Stealthy Targeted Poisoning Attack

Deep learning is widely applied to various areas for its great performance. However, it is vulnerable to adversarial attacks and poisoning attacks, which arouses a lot of concerns. A number of attack methods and defense strategies have been proposed, most of which focus on adversarial attacks that happen in the testing process. Poisoning attacks, using poisoned-training data to attack deep learning models, are more difficult to defend since the models heavily depend on the training data and strategies to guarantee their performances. Generally, poisoning attacks are conducted by leveraging benign examples with poisoned labels or poison-training examples with benign labels. Both cases are easy to detect. In this paper, we propose a novel poisoning attack named Invisible Poisoning Attack (IPA). In IPA, we use highly stealthy poison-training examples with benign labels, perceptually similar to their benign counterparts, to train the deep learning model. During the testing process, the poisoned model will handle the benign examples correctly, while output erroneous results when fed by the target benign examples (poisoning-trigger examples). We adopt the Non-dominated Sorting Genetic Algorithm (NSGA-II) as the optimizer for evolving the highly stealthy poison-training examples. The generated approximate optimal examples are promised to be both invisible and effective in attacking the target model. We verify the effectiveness of IPA against face recognition systems on different face datasets, including attack ability, stealthiness, and transferability performance.