Fetching the paper…
Reading the bibliography…
Despite a great deal of research, it is still unclear why neural networks are so susceptible to adversarial examples.
Nonsmooth analysis and control theory , volume 178
F. H. Clarke, Y. S. Ledyaev, R. J. Stern, and P. R. Wolenski · 2008
Earlier work this paper cites.
Approximate kkt points and a proximity measure for termination
J. Dutta, K. Deb, R. Tulshyan, and R. Arora · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
Earlier work this paper cites.
A survey on universal adversarial attack
C. Zhang, P. Benz, C. Lin, A. Karjauv, J. Wu, and I. S. Kweon · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
Earlier work this paper cites.
Delving into transferable adversarial examples and black-box attacks
Y. Liu, X. Chen, C. Liu, and D. Song · 2016
Earlier work this paper cites.
Distillation as a defense to adversarial perturbations against deep neural networks
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami · 2016
Earlier work this paper cites.
Adversarial examples are not easily detected: Bypassing ten detection methods
N. Carlini and D. Wagner · 2017
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2017
Earlier work this paper cites.
Universal adversarial perturbations
S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 2017
Earlier work this paper cites.
Practical black-box attacks against machine learning
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami · 2017
Earlier work this paper cites.
Threat of adversarial attacks on deep learning in computer vision: A survey
N. Akhtar and A. Mian · 2018
Earlier work this paper cites.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
A. Athalye, N. Carlini, and D. Wagner · 2018
Earlier work this paper cites.
Audio adversarial examples: Targeted attacks on speech-to-text
N. Carlini and D. Wagner · 2018
Cited alongside, same era.
Symmetric multivariate and related distributions
K. W. Fang · 2018
Cited alongside, same era.
Adversarial vulnerability for any classifier
A. Fawzi, H. Fawzi, and O. Fawzi · 2018
Cited alongside, same era.
On the geometry of adversarial examples
M. Khoury and D. Hadfield-Menell · 2018
Cited alongside, same era.
Adversarially robust generalization requires more data
L. Schmidt, S. Santurkar, D. Tsipras, K. Talwar, and A. Madry · 2018
Cited alongside, same era.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
F. Croce and M. Hein · 2020
Later among the works it cites.
Most relu networks suffer from ℓ 2 \ell_{2} adversarial perturbations
A. Daniely and H. Shacham · 2020
Later among the works it cites.
Directional convergence and alignment in deep learning
Z. Ji and M. Telgarsky · 2020
Later among the works it cites.
The pitfalls of simplicity bias in neural networks
H. Shah, K. Tamuly, A. Raghunathan, P. Jain, and P. Netrapalli · 2020
Later among the works it cites.
High-frequency component helps explain the generalization of convolutional neural networks
H. Wang, X. Wu, Z. Huang, and E. P. Xing · 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…
A. Shafahi, W. R. Huang, C. Studer, S. Feizi, and T. Goldstein · 2018
Cited alongside, same era.
Provable defenses against adversarial examples via the convex outer adversarial polytope
E. Wong and Z. Kolter · 2018
Cited alongside, same era.
Adversarial examples from computational constraints
S. Bubeck, Y. T. Lee, E. Price, and I. Razenshteyn · 2019
Cited alongside, same era.
Gradient descent maximizes the margin of homogeneous neural networks
K. Lyu and J. Li · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al · 2019
Cited alongside, same era.
A simple explanation for the existence of adversarial examples with small hamming distance
A. Shamir, I. Safran, E. Ronen, and O. Dunkelman · 2019
Cited alongside, same era.
Feature purification: How adversarial training performs robust deep learning
Z. Allen-Zhu and Y. Li · 2020
Cited alongside, same era.
Making an invisibility cloak: Real world adversarial attacks on object detectors
Z. Wu, S.-N. Lim, L. S. Davis, and T. Goldstein · 2020
Later among the works it cites.
Adversarial examples in multi-layer random relu networks
P. Bartlett, S. Bubeck, and Y. Cherapanamjeri · 2021
Later among the works it cites.
A single gradient step finds adversarial examples on random two-layers neural networks
S. Bubeck, Y. Cherapanamjeri, G. Gidel, and R. T. d. Combes · 2021
Later among the works it cites.
Shift invariance can reduce adversarial robustness
S. Ge, V. Singla, R. Basri, and D. Jacobs · 2021
Later among the works it cites.
The dimpled manifold model of adversarial examples in machine learning
A. Shamir, O. Melamed, and O. BenShmuel · 2021
Later among the works it cites.
On margin maximization in linear and relu networks
G. Vardi, O. Shamir, and N. Srebro · 2021
Later among the works it cites.
On the implicit bias in deep-learning algorithms
G. Vardi · 2022
Closest in time.