Headless Horseman: Adversarial Attacks on Transfer Learning Models

Authors: Ahmed Abdelkader, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, Chen Zhu
Published: 2020-04-20

Source: https://arxiv.org/abs/2004.09007

PDF: https://arxiv.org/pdf/2004.09007

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ポイズニング 敵対的摂動手法 機械学習

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Abstract

Transfer learning facilitates the training of task-specific classifiers using pre-trained models as feature extractors. We present a family of transferable adversarial attacks against such classifiers, generated without access to the classification head; we call these headless attacks. We first demonstrate successful transfer attacks against a victim network using only its feature extractor. This motivates the introduction of a label-blind adversarial attack. This transfer attack method does not require any information about the class-label space of the victim. Our attack lowers the accuracy of a ResNet18 trained on CIFAR10 by over 40%.