Journal
2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
Volume -, Issue -, Pages 2516-2520Publisher
IEEE
DOI: 10.1109/icip.2019.8803269
Keywords
Adversarial example attack; generative adversarial networks; deep neural networks; face recognition
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Deep Learning models have been developed rapidly and achieved great success in computer vision and natural language processing. In this paper, we propose to generate adversarial examples to attack well-trained face recognition models by applying makeup effect to face images. It consists of two generative adversarial networks (GANs) based sub-networks, Makeup Transfer Sub-network and Adversarial Attack Sub-network. Makeup Transfer Sub-network transfers the non-makeup face images to makeup faces. Adversarial Attack Sub-networks hides attack information within makeup effect. The generated face images make the well-trained face recognition models misclassified as dodge attack or target attack. The experimental results demonstrate that our method can generate high-quality face makeup images and achieve higher error rates on various face recognition models compared to the existing attack methods.
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