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Face recognition has achieved great success in the last five years due to the development of deep learning methods.
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C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, and I. Goodfellow, “Intriguing properties of neural networks,” in International Conference on Learning Representation , 2014
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2014
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Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “Deepface: Closing the gap to human-level performance in face verification,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2014, pp. 1701–1708
2014
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N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The journal of machine learning research , vol. 15, no. 1, pp. 1929–1958, 2014
2014
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F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823
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I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” in International Conference on Learning Representation , 2015
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2015
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K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in International Conference on Learning Representation , 2015
2015
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C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 1–9
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Y. Wen, K. Zhang, Z. Li, and Y. Qiao, “A discriminative feature learning approach for deep face recognition,” in European conference on computer vision . Springer, 2016, pp. 499–515
2016
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I. Kemelmacher-Shlizerman, S. M. Seitz, D. Miller, and E. Brossard, “The megaface benchmark: 1 million faces for recognition at scale,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 4873–4882
2016
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M. Sharif, S. Bhagavatula, L. Bauer, and M. K. Reiter, “Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2016, pp. 1528–1540
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Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao, “Ms-celeb-1m: A dataset and benchmark for large-scale face recognition,” in European Conference on Computer Vision . Springer, 2016, pp. 87–102
2016
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
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S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: a simple and accurate method to fool deep neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2574–2582
2016
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S. Sabour, Y. Cao, F. Faghri, and D. J. Fleet, “Adversarial manipulation of deep representations,” in International Conference on Learning Representation , 2016
2016
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W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song, “Sphereface: Deep hypersphere embedding for face recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 212–220
2017
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Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, “Vggface2: A dataset for recognising faces across pose and age,” in 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) . IEEE, 2018, pp. 67–74
2018
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F. Wang, L. Chen, C. Li, S. Huang, Y. Chen, C. Qian, and C. Change Loy, “The devil of face recognition is in the noise,” in European conference on computer vision , 2018, pp. 765–780
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J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7132–7141
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F. Tramer, A. Kurakin, N. Papernot, D. Boneh, and P. McDaniel, “Ensemble adversarial training: Attacks and defenses,” in International Conference on Learning Representation , 2018
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B. Chen, W. Deng, and J. Du, “Noisy softmax: Improving the generalization ability of dcnn via postponing the early softmax saturation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 5372–5381
2017
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W. Deng, J. Hu, N. Zhang, B. Chen, and J. Guo, “Fine-grained face verification: Fglfw database, baselines, and human-dcmn partnership,” Pattern Recognition , vol. 66, pp. 63–73, 2017
2017
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Y. Liu, X. Chen, C. Liu, and D. Song, “Delving into transferable adversarial examples and black-box attacks,” in International Conference on Learning Representation , 2017
2017
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N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical black-box attacks against machine learning,” in Proceedings of the 2017 ACM on Asia conference on computer and communications security . ACM, 2017, pp. 506–519
2017
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A. Rozsa, M. Günther, and T. E. Boult, “Lots about attacking deep features,” in 2017 IEEE International Joint Conference on Biometrics (IJCB) . IEEE, 2017, pp. 168–176
2017
Cited alongside, same era.
2017
Cited alongside, same era.
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-v4, inception-resnet and the impact of residual connections on learning,” in Thirty-First AAAI Conference on Artificial Intelligence , 2017
2017
Cited alongside, same era.
A. Kurakin, I. J. Goodfellow, and S. Bengio, “Adversarial machine learning at scale,” in International Conference on Learning Representation , 2017
2017
Cited alongside, same era.
K. R. Mopuri, A. Ganeshan, and R. V. Babu, “Generalizable data-free objective for crafting universal adversarial perturbations,” IEEE transactions on pattern analysis and machine intelligence , vol. 41, no. 10, pp. 2452–2465, 2018
2018
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M. Wang and W. Deng, “Deep face recognition: A survey,” arXiv preprint arXiv:1804.06655 , 2018
2018
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Y. Zheng, D. K. Pal, and M. Savvides, “Ring loss: Convex feature normalization for face recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 5089–5097
2018
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G. Goswami, N. Ratha, A. Agarwal, R. Singh, and M. Vatsa, “Unravelling robustness of deep learning based face recognition against adversarial attacks,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018
2018
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2018
Later among the works it cites.
J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 4690–4699
2019
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C. Xie, Z. Zhang, Y. Zhou, S. Bai, J. Wang, Z. Ren, and A. L. Yuille, “Improving transferability of adversarial examples with input diversity,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 2730–2739
2019
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Y. Dong, H. Su, B. Wu, Z. Li, W. Liu, T. Zhang, and J. Zhu, “Efficient decision-based black-box adversarial attacks on face recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 7714–7722
2019
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N. Inkawhich, W. Wen, H. H. Li, and Y. Chen, “Feature space perturbations yield more transferable adversarial examples,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 7066–7074
2019
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Q. Huang, I. Katsman, H. He, Z. Gu, S. Belongie, and S.-N. Lim, “Enhancing adversarial example transferability with an intermediate level attack,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 4733–4742
2019
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Y. Dong, T. Pang, H. Su, and J. Zhu, “Evading defenses to transferable adversarial examples by translation-invariant attacks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 4312–4321
2019
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X. Zhang, R. Zhao, Y. Qiao, X. Wang, and H. Li, “Adacos: Adaptively scaling cosine logits for effectively learning deep face representations,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 10 823–10 832
2019
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J. Deng, J. Guo, D. Zhang, Y. Deng, X. Lu, and S. Shi, “Lightweight face recognition challenge,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , 2019, pp. 0–0
2019
Later among the works it cites.
2019
Later among the works it cites.
G. Goswami, A. Agarwal, N. Ratha, R. Singh, and M. Vatsa, “Detecting and mitigating adversarial perturbations for robust face recognition,” International Journal of Computer Vision , vol. 127, no. 6-7, pp. 719–742, 2019
2019
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Y. Zhong and W. Deng, “Adversarial learning with margin-based triplet embedding regularization,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 6549–6558
2019
Later among the works it cites.
D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Madry, “Robustness may be at odds with accuracy,” in International Conference on Learning Representation , 2019
2019
Later among the works it cites.
D. Wu, Y. Wang, S.-T. Xia, J. Bailey, and X. Ma, “Skip connections matter: On the transferability of adversarial examples generated with resnets,” in International Conference on Learning Representation , 2020
2020
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