Journal of the Korea Institute of Military Science and Technology (한국군사과학기술학회지)

The Korea Institute of Military Science and Technology (한국군사과학기술학회)
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Adversarial Attacks for Deep Learning-Based Infrared Object Detection

딥러닝 기반 적외선 객체 검출을 위한 적대적 공격 기술 연구

  • Kim, Hoseong (The 1st Research and Development Institute, Agency for Defense Development) ;
  • Hyun, Jaeguk (The 1st Research and Development Institute, Agency for Defense Development) ;
  • Yoo, Hyunjung (The 1st Research and Development Institute, Agency for Defense Development) ;
  • Kim, Chunho (The 1st Research and Development Institute, Agency for Defense Development) ;
  • Jeon, Hyunho (Satellite & Space Exploration Systems Engineering and Architecture R&D Division, Korea Aerospace Research Institute)
  • 김호성 (국방과학연구소 미사일연구원) ;
  • 현재국 (국방과학연구소 미사일연구원) ;
  • 유현정 (국방과학연구소 미사일연구원) ;
  • 김춘호 (국방과학연구소 미사일연구원) ;
  • 전현호 (한국항공우주연구원 위성우주탐사체계설계부)
  • Received : 2021.06.23
  • Accepted : 2021.11.19
  • Published : 2021.12.05
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Abstract

Recently, infrared object detection(IOD) has been extensively studied due to the rapid growth of deep neural networks(DNN). Adversarial attacks using imperceptible perturbation can dramatically deteriorate the performance of DNN. However, most adversarial attack works are focused on visible image recognition(VIR), and there are few methods for IOD. We propose deep learning-based adversarial attacks for IOD by expanding several state-of-the-art adversarial attacks for VIR. We effectively validate our claim through comprehensive experiments on two challenging IOD datasets, including FLIR and MSOD.

Keywords

References

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