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Trojan (backdoor) attack is a form of adversarial attack on deep neuralnetworks where the attacker provides victims with a model trained/retrained onmalicious data. The backdoor can be activated when a normal input is stampedwith a certain pattern called trigger, causing misclassification. Many existingtrojan attacks have their triggers being input space patches/objects (e.g., apolygon with solid color) or simple input transformations such as Instagramfilters. These simple triggers are susceptible to recent backdoor detectionalgorithms. We propose a novel deep feature space trojan attack with fivecharacteristics: effectiveness, stealthiness, controllability, robustness andreliance on deep features. We conduct extensive experiments on 9 imageclassifiers on various datasets including ImageNet to demonstrate theseproperties and show that our attack can evade state-of-the-art defense.

Deep Feature Space Trojan Attack of Neural Networks by Controlled Detoxification