Practical Attacks on Deep Neural Networks by Memory Trojaning

Abstract

Deep neural network (DNN) accelerators are widely deployed in computer vision, speech recognition, and machine translation applications, in which attacks on DNNs have become a growing concern. This article focuses on exploring the implications of hardware Trojan attacks on DNNs. Trojans are one of the most challenging threat models in hardware security where adversaries insert malicious modifications to the original integrated circuits (ICs), leading to malfunction once being triggered. Such attacks can be conducted by adversaries because modern ICs commonly include third-party intellectual property (IP) blocks. Previous studies design hardware Trojans to attack DNNs with the assumption that adversaries have full knowledge or manipulation of the DNN systems' victim model and toolchain in addition to the hardware platforms, yet such a threat model is strict, limiting their practical adoption. In this article, we propose a memory Trojan methodology that implants the malicious logics merely into the memory controllers of DNN systems without the necessity of toolchain manipulation or accessing to the victim model and thus is feasible for practical uses. Specifically, we locate the input image data among the massive volume of memory traffics based on memory access patterns and propose a Trojan trigger mechanism based on detecting the geometric feature in input images. Extensive experiments show that the proposed trigger mechanism is effective even in the presence of environmental noises and preprocessing operations. Furthermore, we design and implement the payload and verify that the proposed Trojan technique can effectively conduct both untargeted and targeted attacks on DNNs.