Toward Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-based Method

Tao Bai, Jun Zhao, Jinlin Zhu, Shoudong Han, Jiefeng Chen, Bo Li, Alex Kot

Research output: Contribution to journalArticlepeer-review


Intelligent Internet of Things (IoT) systems based on deep neural networks (DNNs) have been widely deployed in the real world. However, DNNs are found to be vulnerable to adversarial examples, which raises people's concerns about intelligent IoT systems' reliability and security. Testing and evaluating the robustness of IoT systems become necessary and essential. Recently, various attacks and strategies have been proposed, but the efficiency problem remains unsolved properly. Existing methods are either computationally extensive or time consuming, which is not applicable in practice. In this article, we propose a novel framework, called attack-inspired generative adversarial networks (AI-GAN) to generate adversarial examples conditionally. Once trained, it can generate adversarial perturbations efficiently given input images and target classes. We apply AI-GAN on different data sets in white-box settings, black-box settings, and targeted models protected by state-of-the-art defenses. Through extensive experiments, AI-GAN achieves high attack success rates, outperforming existing methods, and reduces generation time significantly. Moreover, for the first time, AI-GAN successfully scales to complex data sets, e.g., CIFAR-100 and ImageNet, with about 90% success rates among all classes.

Original languageEnglish (US)
Pages (from-to)1875-1884
Number of pages10
JournalIEEE Internet of Things Journal
Issue number3
StatePublished - Feb 1 2022


  • Adversarial examples
  • Deep learning
  • GAN.
  • Generative adversarial networks
  • Generators
  • Internet of Things
  • Neural networks
  • Optimization
  • Perturbation methods
  • Training

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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