Amora: Black-box Adversarial Morphing Attack

Run Wang, Felix Juefei-Xu, Qing Guo, Yihao Huang, Xiaofei Xie, Lei Ma, Yang Liu

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

10 Citations (Scopus)


Nowadays, digital facial content manipulation has become ubiquitous and realistic with the success of generative adversarial networks (GANs), making face recognition (FR) systems suffer from unprecedented security concerns. In this paper, we investigate and introduce a new type of adversarial attack to evade FR systems by manipulating facial content, called adversarial morphing attack (a.k.a. Amora). In contrast to adversarial noise attack that perturbs pixel intensity values by adding human-imperceptible noise, our proposed adversarial morphing attack works at the semantic level that perturbs pixels spatially in a coherent manner. To tackle the black-box attack problem, we devise a simple yet effective joint dictionary learning pipeline to obtain a proprietary optical flow field for each attack. Our extensive evaluation on two popular FR systems demonstrates the effectiveness of our adversarial morphing attack at various levels of morphing intensity with smiling facial expression manipulations. Both open-set and closed-set experimental results indicate that a novel black-box adversarial attack based on local deformation is possible, and is vastly different from additive noise attacks. The findings of this work potentially pave a new research direction towards a more thorough understanding and investigation of image-based adversarial attacks and defenses.

Original languageEnglish
Title of host publicationMM 2020 - Proceedings of the 28th ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Number of pages10
ISBN (Electronic)9781450379885
Publication statusPublished - Oct 12 2020
Event28th ACM International Conference on Multimedia, MM 2020 - Virtual, Online, United States
Duration: Oct 12 2020Oct 16 2020

Publication series

NameMM 2020 - Proceedings of the 28th ACM International Conference on Multimedia


Conference28th ACM International Conference on Multimedia, MM 2020
Country/TerritoryUnited States
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction


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