Fetching the paper…
Reading the bibliography…
Deep learning models are known to be vulnerable to adversarial examples that are elaborately designed for malicious purposes and are imperceptible to the human perceptual system.
C. Szegedy, W. Zaremba, I. Sutskever, and et al., “Intriguing properties of neural networks,” in ICLR
2013
Earlier work this paper cites.
R. Socher, A. Perelygin, J. Wu, and et al., “Recursive deep models for semantic compositionality over a sentiment treebank,” in EMNLP
2013
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, C. Szegedy, and et al., “Explaining and harnessing adversarial examples,” in ICLR
2015
Earlier work this paper cites.
G. E. Hinton, O. Vinyals, J. Dean, and et al., “Distilling the knowledge in a neural network,” ArXiv
2015
Earlier work this paper cites.
K. Simonyan, A. Zisserman, and et al., “Very deep convolutional networks for large-scale image recognition,” in ICLR
2015
Earlier work this paper cites.
X. Zhang, J. J. Zhao, Y. LeCun, and et al., “Character-level convolutional networks for text classification,” in NeurIPS
2015
Earlier work this paper cites.
S.-M. Moosavi-Dezfooli, A. Fawzi, P. Frossard, and et al., “DeepFool: A simple and accurate method to fool deep neural networks,” in CVPR
2016
Earlier work this paper cites.
D. Hendrycks, K. Gimpel, and et al., “Early methods for detecting adversarial images,” in ICLR
2016
Earlier work this paper cites.
X. Chen, Y. Duan, R. Houthooft, and et al., “Infogan: Interpretable representation learning by information maximizing generative adversarial nets,” in NeurIPS
2016
Earlier work this paper cites.
N. Carlini, D. Wagner, and et al., “Towards evaluating the robustness of neural networks,” in Oakland
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
N. Carlini, D. Wagner, and et al., “Adversarial examples are not easily detected: Bypassing ten detection methods,” in CCS
2017
Earlier work this paper cites.
D. Meng, H. Chen, and et al., “Magnet: A two-pronged defense against adversarial examples,” in CCS
2017
Earlier work this paper cites.
N. Carlini, D. Wagner, and et al., “Adversarial examples are not easily detected: Bypassing ten detection methods,” in Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security
2017
Earlier work this paper cites.
J. Lu, T. Issaranon, D. Forsyth, and et al., “Safetynet: Detecting and rejecting adversarial examples robustly,” in ICCV
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
L. Tran, X. Yin, X. Liu, and et al., “Disentangled representation learning gan for pose-invariant face recognition,” in CVPR
2017
Cited alongside, same era.
Y. Qin, N. Frosst, S. Sabour, and et al., “Detecting and diagnosing adversarial images with class-conditional capsule reconstructions,” in ICLR
2019
Later among the works it cites.
A. Ilyas, S. Santurkar, D. Tsipras, and et al., “Adversarial examples are not bugs, they are features,” in NeurIPS
2019
Later among the works it cites.
S. Ren, Y. Deng, K. He, and et al., “Generating natural language adversarial examples through probability weighted word saliency,” in ACL
2019
Later among the works it cites.
J. Devlin, M.-W. Chang, K. Lee, and et al., “BERT: Pre-training of deep bidirectional transformers for language understanding,” in NAACL
2019
Later among the works it cites.
Z. Lan, M. Chen, S. Goodman, and et al., “ALBERT: A lite bert for self-supervised learning of language representations,” in ICLR
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
J. Rauber, W. Brendel, M. Bethge, and et al., “Foolbox: A python toolbox to benchmark the robustness of machine learning models,” in ICML
2017
Cited alongside, same era.
A. Madry, A. Makelov, L. Schmidt, and et al., “Towards deep learning models resistant to adversarial attacks,” in ICLR
2018
Cited alongside, same era.
K. Lee, K. Lee, H. Lee, and et al., “A simple unified framework for detecting out-of-distribution samples and adversarial attacks,” in NeurIPS
2018
Cited alongside, same era.
L. Schott, J. Rauber, M. Bethge, and et al., “Towards the first adversarially robust neural network model on MNIST,” in ICLR
2018
Cited alongside, same era.
X. Ma, B. Li, Y. Wang, and et al., “Characterizing adversarial subspaces using local intrinsic dimensionality,” in ICLR
2018
Cited alongside, same era.
M. Alzantot, Y. Sharma, A. Elgohary, and et al., “Generating natural language adversarial examples,” in EMNLP
2018
Cited alongside, same era.
D. M. Cer, Y. Yang, S. yi Kong, and et al., “Universal sentence encoder for english,” in EMNLP
2018
Cited alongside, same era.
C. Chen, J. Liu, Y. Xie, Y. X. Ban, and et al., “Latent Regularized Generative Dual Adversarial Network For Abnormal Detection,” in IJCAI
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
S. Hu, Y. Zhang, X. Liu, L. Y. Zhang, M. Li, and H. Jin, “Advhash: Set-to-set targeted attack on deep hashing with one single adversarial patch,” in ACM MM
2021
Closest in time.
J. Tian, J. Zhou, Y. Li, and et al., “Detecting adversarial examples from sensitivity inconsistency of spatial-transform domain,” in AAAI
2021
Closest in time.
K. Yang, T. Zhou, Y. Zhang, X. Tian, and D. Tao, “Class-disentanglement and applications in adversarial detection and defense,” in NeurIPS
2021
Closest in time.
S. Hu, X. Liu, Y. Zhang, M. Li, L. Y. Zhang, H. Jin, and L. Wu, “Protecting facial privacy: Generating adversarial identity masks via style-robust makeup transfer,” in CVPR
2022
Closest in time.
Z. Zhang, L. Y. Zhang, X. Zheng, B. H. Abbasi, and S. Hu, “Evaluating membership inference through adversarial robustness,” The Computer Journal
2022
Closest in time.