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Deep neural networks are known to be vulnerable to adversarial examples, where a perturbation in the input space leads to an amplified shift in the latent network representation.
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Contractive auto-encoders: Explicit invariance during feature extraction
Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio · 2011
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Discriminative unsupervised feature learning with convolutional neural networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Towards deep neural network architectures robust to adversarial examples
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Deep residual learning for image recognition
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Adversarial machine learning at scale
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Temporal ensembling for semi-supervised learning
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Defense against adversarial attacks using high-level representation guided denoiser
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Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, and Nate Kushman · 2018
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Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
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Metric learning for adversarial robustness
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Disentangling adversarial robustness and generalization
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Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P Xing, Laurent El Ghaoui, and Michael I Jordan · 2019
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Momentum contrast for unsupervised visual representation learning
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A self-supervised approach for adversarial robustness
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