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Adversarial Binaries for Authorship Identification
[article]
2018
arXiv
pre-print
Binary code authorship identification determines authors of a binary program. Existing techniques have used supervised machine learning for this task. In this paper, we look this problem from an attacker's perspective. We aim to modify a test binary, such that it not only causes misprediction but also maintains the functionality of the original input binary. Attacks against binary code are intrinsically more difficult than attacks against domains such as computer vision, where attackers can
arXiv:1809.08316v2
fatcat:lc67mss26fdtnhws5zk2xyavga