FaDec: A Fast Decision-based Attack for Adversarial Machine Learning

Faiq Khalid, Hassan Ali, Muhammad Abdullah Hanif, Semeen Rehman, Rehan Ahmed, Muhammad Shafique

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Due to the excessive use of cloud-based machine learning (ML) services, the smart cyber-physical systems (CPS) are increasingly becoming vulnerable to black-box attacks on their ML modules. Traditionally, the black-box attacks are either transfer attacks requiring model stealing, or score/decision-based gradient estimation attacks requiring a large number of queries. In practical scenarios, especially for cloud-based ML services and timing-constrained CPS use-cases, every query incurs a huge cost, thereby rendering state-of-the-art decision-based attacks ineffective in such settings. Towards this, we propose a novel methodology for automatically generating an extremely fast and imperceptible decision-based attack called FaDec. It follows two main steps: (1) fast estimation of the classification boundary by combining the half-interval search-based algorithm with gradient sign estimation to reduce the number of queries; and (2) adversarial noise optimization to ensure the imperceptibility. For illustration, we evaluate FaDec on the image recognition and traffic sign detection using multiple state-of-the-art DNNs trained on CIFAR-10 and the German Traffic Sign Recognition Benchmarks (GTSRB) datasets. The experimental analysis shows that the proposed FaDec attack is 16x faster compared to the state-of-the-art decision-based attacks, and generates an attack image with better imperceptibility for a much lesser number of iterations, thereby making our attack more powerful in practical scenarios. We open-sourced the complete code and results of our methodology at https://github.com/fklodhi/FaDec.

Original languageEnglish (US)
Title of host publication2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728169262
StatePublished - Jul 2020
Event2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom
Duration: Jul 19 2020Jul 24 2020

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Conference2020 International Joint Conference on Neural Networks, IJCNN 2020
CountryUnited Kingdom
CityVirtual, Glasgow

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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