Adversarial Perturbation Attacks on ML-based CAD: A Case Study on CNN-based Lithographic Hotspot Detection

Kang Liu, Haoyu Yang, Yuzhe Ma, Benjamin Tan, Bei Yu, Evangeline F.Y. Young, Ramesh Karri, Siddharth Garg

Research output: Contribution to journalArticlepeer-review

Abstract

There is substantial interest in the use of machine learning (ML)-based techniques throughout the electronic computer-aided design (CAD) flow, particularly those based on deep learning. However, while deep learning methods have surpassed state-of-the-art performance in several applications, they have exhibited intrinsic susceptibility to adversarial perturbations - small but deliberate alterations to the input of a neural network, precipitating incorrect predictions. In this article, we seek to investigate whether adversarial perturbations pose risks to ML-based CAD tools, and if so, how these risks can be mitigated. To this end, we use a motivating case study of lithographic hotspot detection, for which convolutional neural networks (CNN) have shown great promise. In this context, we show the first adversarial perturbation attacks on state-of-the-art CNN-based hotspot detectors; specifically, we show that small (on average 0.5% modified area), functionality preserving, and design-constraint-satisfying changes to a layout can nonetheless trick a CNN-based hotspot detector into predicting the modified layout as hotspot free (with up to 99.7% success in finding perturbations that flip a detector's output prediction, based on a given set of attack constraints). We propose an adversarial retraining strategy to improve the robustness of CNN-based hotspot detection and show that this strategy significantly improves robustness (by a factor of ∼3) against adversarial attacks without compromising classification accuracy.

Original languageEnglish (US)
Article number3408288
JournalACM Transactions on Design Automation of Electronic Systems
Volume25
Issue number5
DOIs
StatePublished - Oct 2020

Keywords

  • adversarial perturbations
  • lithographic hotspot detection
  • ML-based CAD
  • security

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

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