CAPTIVE: Constrained Adversarial Perturbations to Thwart IC Reverse Engineering
This addresses hardware security by preventing IP theft and illicit activities like hardware Trojan insertion, though it is incremental as it builds on adversarial machine learning techniques.
The paper tackles the problem of reverse engineering (RE) in integrated circuits by proposing CAPTIVE, a method to introduce constrained adversarial perturbations to IC layouts, which reduces RE accuracy from 100% to approximately 30%.
Reverse engineering (RE) in Integrated Circuits (IC) is a process in which one will attempt to extract the internals of an IC, extract the circuit structure, and determine the gate-level information of an IC. In general, RE process can be done for validation as well as intellectual property (IP) stealing intentions. In addition, RE also facilitates different illicit activities such as insertion of hardware Trojan, pirate, or counterfeit a design, or develop an attack. In this work, we propose an approach to introduce cognitive perturbations, with the aid of adversarial machine learning, to the IC layout that could prevent the RE process from succeeding. We first construct a layer-by-layer image dataset of 45nm predictive technology. With this dataset, we propose a conventional neural network model called RecoG-Net to recognize the logic gates, which is the first step in RE. RecoG-Net is successfully to recognize the gates with more than 99.7% accuracy. Our thwarting approach utilizes the concept of the adversarial attack generation algorithms to generate perturbation. Unlike traditional adversarial attacks in machine learning, the perturbation generation needs to be highly constrained to meet the fab rules such as Design Rule Checking (DRC) Layout vs. Schematic (LVS) checks. Hence, we propose CAPTIVE as an constrained perturbation generation satisfying the DRC. The experiments shows that the accuracy of reverse engineering using machine learning techniques can decrease from 100% to approximately 30% based on the adversary generator.