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Certified defenses against adversarial attacks offer formal guarantees on the robustness of a model, making them more reliable than empirical methods such as adversarial training, whose effectiveness is often later reduced by unseen attacks.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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RobustBench: a standardized adversarial robustness benchmark
F. Croce, M. Andriushchenko, V. Sehwag, E. Debenedetti, N. Flammarion, M. Chiang, P. Mittal, and M. Hein · 2010
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ImageNet Classification with Deep Convolutional Neural Networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus · 2014
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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SGDR: Stochastic Gradient Descent with Warm Restarts
I. Loshchilov and F. Hutter · 2017
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Towards Deep Learning Models Resistant to Adversarial Attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2018
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Certified Adversarial Robustness via Randomized Smoothing
J. M. Cohen, E. Rosenfeld, and J. Z. Kolter · 2019
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Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
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Theoretically principled trade-off between robustness and accuracy
H. Zhang, Y. Yu, J. Jiao, E. P. Xing, L. El Ghaoui, and M. I. Jordan · 2019
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Denoising Diffusion Probabilistic Models
J. Ho, A. Jain, and P. Abbeel · 2020
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CLIP: Cheap lipschitz training of neural networks
L. Bungert, R. Raab, T. Roith, L. Schwinn, and D. Tenbrinck · 2021
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Diffusion Models Beat GANs on Image Synthesis
P. Dhariwal and A. Nichol · 2021
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Improving Robustness using Generated Data
S. Gowal, S. Rebuffi, O. Wiles, F. Stimberg, D. A. Calian, and T. A. Mann · 2021
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Elucidating the Design Space of Diffusion-Based Generative Models
T. Karras, M. Aittala, T. Aila, and S. Laine · 2022
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Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification
L. Schwinn, R. Raab, A. Nguyen, D. Zanca, and B. M. Eskofier · 2022
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LOT: Layer-wise Orthogonal Training on Improving l2 Certified Robustness
X. Xu, L. Li, and B. Li · 2022
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Scaling in Depth: Unlocking Robustness Certification on ImageNet
K. Hu, A. Zou, Z. Wang, K. Leino, and M. Fredrikson · 2023
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Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models
D. Kim, Y. Kim, S. J. Kwon, W. Kang, and I.-C. Moon · 2023
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Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks
L. Schwinn, R. Raab, A. Nguyen, D. Zanca, and B. M. Eskofier · 2021
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CAT: Customized Adversarial Training for Improved Robustness
M. Cheng, Q. Lei, P. Chen, I. S. Dhillon, and C. Hsieh · 2022
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Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective
B. Zhang, D. Jiang, D. He, and L. Wang
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Boosting the Certified Robustness of L-infinity Distance Nets
B. Zhang, D. Jiang, D. He, and L. Wang
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SoK: Certified Robustness for Deep Neural Networks
L. Li, T. Xie, and B. Li · 2023
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Better Diffusion Models Further Improve Adversarial Training
Z. Wang, T. Pang, C. Du, M. Lin, W. Liu, and S. Yan · 2023
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