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It is well known that adversarial attacks can fool deep neural networks with imperceptible perturbations.
Saliency methods for explaining adversarial attacks
Gu, J · 1908
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An alternative surrogate loss for pgd-based adversarial testing
Gowal, S · 1910
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On pruning adversarially robust neural networks
Sehwag, V · 2002
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Improving adversarial robustness through progressive hardening
Sitawarin, C · 2003
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Image quality assessment: from error visibility to structural similarity
Wang, Z · 2004
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Imagenet: A large-scale hierarchical image database
Deng, J · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Efficient robust training via backward smoothing
Chen, J · 2010
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Robustbench: a standardized adversarial robustness benchmark
Croce, F · 2010
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Uncovering the limits of adversarial training against norm-bounded adversarial examples
Gowal, S · 2010
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MNIST handwritten digit database
LeCun, Y · 2010
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Does network width really help adversarial robustness?
Wu, B · 2010
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Geometry-aware instance-reweighted adversarial training
Zhang, J · 2010
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Learnable boundary guided adversarial training
Cui, J · 2011
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Intriguing properties of neural networks
Szegedy, C · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J · 2015
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Deepfool: A simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S · 2016
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Towards evaluating the robustness of neural networks
Carlini, N · 2017
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ZOO: zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Chen, P · 2017
Cited alongside, same era.
Parseval networks: Improving robustness to adversarial examples
Cissé, M · 2017
Cited alongside, same era.
The space of transferable adversarial examples
Tramèr, F · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A · 2018
Cited alongside, same era.
Stochastic activation pruning for robust adversarial defense
Dhillon, G. S · 2018
Cited alongside, same era.
Countering adversarial images using input transformations
Guo, C · 2018
Disentangling adversarial robustness and generalization
Stutz, D · 2019
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Robustness may be at odds with accuracy
Tsipras, D · 2019
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Square attack: A query-efficient black-box adversarial attack via random search
Andriushchenko, M · 2020
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Jacobian adversarially regularized networks for robustness
Chan, A · 2020
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Rays: A ray searching method for hard-label adversarial attack
Chen, J · 2020
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MMA training: Direct input space margin maximization through adversarial training
Ding, G. W · 2020
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Cited alongside, same era.
Visualizing the loss landscape of neural nets
Li, H · 2018
Cited alongside, same era.
Adversarial detection with model interpretation
Liu, N · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A · 2018
Cited alongside, same era.
Towards robust detection of adversarial examples
Pang, T · 2018
Cited alongside, same era.
Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
Ross, A. S · 2018
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Tramèr, F · 2018
Cited alongside, same era.
Benchmarking adversarial robustness on image classification
Dong, Y · 2020
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Self-adaptive training: beyond empirical risk minimization
Huang, L · 2020
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A game theoretic analysis of additive adversarial attacks and defenses
Pal, A · 2020
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Boosting adversarial training with hypersphere embedding
Pang, T · 2020
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Randomization matters how to defend against strong adversarial attacks
Pinot, R · 2020
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Overfitting in adversarially robust deep learning
Rice, L · 2020
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Fundamental tradeoffs between invariance and sensitivity to adversarial perturbations
Tramèr, F · 2020
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Improving adversarial robustness requires revisiting misclassified examples
Wang, Y · 2020
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Fast is better than free: Revisiting adversarial training
Wong, E · 2020
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Attacks which do not kill training make adversarial learning stronger
Zhang, J · 2020
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Effective and efficient vote attack on capsule networks
Gu, J · 2021
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