Fetching the paper…
Reading the bibliography…
It is becoming increasingly imperative to design robust ML defenses.
Jia, J., Cao, X., Wang, B., and Gong, N. Z · 1912
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
Earlier work this paper cites.
The nag factor
Henry, H. K. M. and Borzekowski, D. L. G · 2011
Earlier work this paper cites.
Bayesian learning via stochastic gradient langevin dynamics
Welling, M. and Teh, Y. W · 2011
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
Earlier work this paper cites.
Adversarial examples are not easily detected: Bypassing ten detection methods
Carlini, N. and Wagner, D · 2017
Earlier work this paper cites.
Foolbox: A python toolbox to benchmark the robustness of machine learning models
Rauber, J., Brendel, W., and Bethge, M · 2017
Earlier work this paper cites.
Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Brendel, W., Rauber, J., and Bethge, M · 2018
Earlier work this paper cites.
Comdefend: An efficient image compression model to defend adversarial examples
Jia, X., Wei, X., Cao, X., and Foroosh, H · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Cited alongside, same era.
Towards robust detection of adversarial examples
Pang, T., Du, C., Dong, Y., and Zhu, J · 2018
Cited alongside, same era.
Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Song, Y., Kim, T., Nowozin, S., Ermon, S., and Kushman, N · 2018
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Cohen, J. M., Rosenfeld, E., and Kolter, J. Z · 2019
Cited alongside, same era.
Benchmarking adversarial robustness
Dong, Y., Fu, Q.-A., Yang, X., Pang, T., Su, H., Xiao, Z., and Zhu, J · 2019
Improving transferability of adversarial examples with input diversity
Xie, C., Zhang, Z., Wang, J., Zhou, Y., Ren, Z., and Yuille, A. L · 2019
Later among the works it cites.
Your classifier is secretly an energy based model and you should treat it like one
Grathwohl, W., Wang, K.-C., and Jacobsen, J.-H · 2020
Later among the works it cites.
Foolbox native: Fast adversarial attacks to benchmark the robustness of machine learning models in pytorch, tensorflow, and jax
Rauber, J., Zimmermann, R., Bethge, M., and Brendel, W · 2020
Later among the works it cites.
On adaptive attacks to adversarial example defenses
Tramèr, F., Carlini, N., Brendel, W., and Mądry, A · 2020
Later among the works it cites.
Enhancing adversarial defense by k-winners-take-all
Xiao, C., Zhong, P., and Zheng, C · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Robustness certificates for sparse adversarial attacks by randomized ablation
Levine, A. and Feizi, S · 2019
Cited alongside, same era.
Barrage of random transforms for adversarially robust defense
Raff, E., Sylvester, J., Forsyth, S., and McLean, M · 2019
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D
Cited in the paper.
Synthesizing robust adversarial examples
Athalye, A., Engstrom, L., Ilyas, A., and Kwok, K
Cited in the paper.
Mitigating adversarial effects through randomization
Xie, C., Wang, J., Zhang, Z., Ren, Z., and Yuille, A
Cited in the paper.
Robustbench: a standardized adversarial robustness benchmark
Croce, F., Andriushchenko, M., Sehwag, V., Debenedetti, E., Flammarion, N., Chiang, M., Mittal, P., and Hein, M · 2021
Later among the works it cites.
On the limitations of stochastic pre-processing defenses
Gao, Y., Shumailov, I., Fawaz, K., and Papernot, N · 2022
Later among the works it cites.
Diffusion models for adversarial purification
Nie, W., Guo, B., Huang, Y., Xiao, C., Vahdat, A., and Anandkumar, A · 2022
Later among the works it cites.