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http://dx.doi.org/10.9766/KIMST.2021.24.6.591

Adversarial Attacks for Deep Learning-Based Infrared Object Detection  

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)
Publication Information
Journal of the Korea Institute of Military Science and Technology / v.24, no.6, 2021 , pp. 591-601 More about this Journal
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
Adversarial Attack; Infrared Object Detection; Deep Learning;
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