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Recent research has demonstrated that adding some imperceptible perturbations to original images can fool deep learning models.
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Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks
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Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
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Defense against adversarial attacks using high-level representation guided denoiser. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 1778–1787
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Towards Deep Learning Models Resistant to Adversarial Attacks. In ICLR (Poster) . OpenReview.net
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ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 6084–6092
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SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing
Haonan Qiu, Chaowei Xiao, Lei Yang, Xinchen Yan, Honglak Lee, and Bo Li. 2019 · 2019
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One pixel attack for fooling deep neural networks
Jiawei Su, Danilo Vasconcellos Vargas, and Kouichi Sakurai. 2019 · 2019
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Efficient Adversarial Attacks for Visual Object Tracking
Siyuan Liang, Xingxing Wei, Siyuan Yao, and Xiaochun Cao. 2020 · 2020
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