Fetching the paper…
Reading the bibliography…
We consider universal adversarial patches for faces -- small visual elements whose addition to a face image reliably destroys the performance of face detectors.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A Large-Scale Hierarchical Image Database. In: CVPR09 (2009)
2009
Earlier work this paper cites.
Jain, V., Learned-Miller, E.: Fddb: A benchmark for face detection in unconstrained settings (2010)
2010
Earlier work this paper cites.
Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., Roli, F.: Evasion attacks against machine learning at test time. In: Joint European conference on machine learning and knowledge discovery in databases. pp. 387–402 (2013)
2013
Earlier work this paper cites.
Yamada, T., Gohshi, S., Echizen, I.: Privacy visor: Method for preventing face image detection by using differences in human and device sensitivity. In: IFIP International Conference on Communications and Multimedia Security. pp. 152–161 (2013)
2013
Earlier work this paper cites.
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. In: International Conference on Learning Representations (ICLR) (2014)
2014
Earlier work this paper cites.
Zitnick, C.L., Dollár, P.: Edge boxes: Locating object proposals from edges. In: European conference on computer vision. pp. 391–405. Springer (2014)
2014
Earlier work this paper cites.
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations (ICLR) (2015)
2015
Earlier work this paper cites.
Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 427–436 (2015)
2015
Earlier work this paper cites.
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems. pp. 91–99 (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: Single shot multibox detector. In: European conference on computer vision. pp. 21–37. Springer (2016)
2016
Earlier work this paper cites.
Papernot, N., McDaniel, P., Wu, X., Jha, S., Swami, A.: Distillation as a defense to adversarial perturbations against deep neural networks. In: IEEE Symposium on Security and Privacy (2016)
2016
Earlier work this paper cites.
Sharif, M., Bhagavatula, S., Bauer, L., Reiter, M.K.: 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. pp. 1528–1540 (2016)
2016
Earlier work this paper cites.
Yang, S., Luo, P., Loy, C.C., Tang, X.: Wider face: A face detection benchmark. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision. pp. 2961–2969 (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial examples in the physical world. In: International Conference on Learning Representations (ICLR) Workshops (2017)
2017
Cited alongside, same era.
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2117–2125 (2017)
2017
Cited alongside, same era.
2019
Closest in time.
Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: Dsfd: dual shot face detector. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5060–5069 (2019)
2019
Closest in time.
2019
Closest in time.
Ming, X., Wei, F., Zhang, T., Chen, D., Wen, F.: Group sampling for scale invariant face detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3446–3456 (2019)
2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980–2988 (2017)
2017
Cited alongside, same era.
Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., Yuille, A.: Adversarial examples for semantic segmentation and object detection. In: IEEE International Conference on Computer Vision. pp. 1369–1378 (2017)
2017
Cited alongside, same era.
You, S., Xu, C., Xu, C., Tao, D.: Learning from multiple teacher networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 1285–1294 (2017)
2017
Cited alongside, same era.
Chen, S.T., Cornelius, C., Martin, J., Chau, D.H.P.: Shapeshifter: Robust physical adversarial attack on faster r-cnn object detector. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. pp. 52–68 (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., Song, D.: Robust physical-world attacks on deep learning visual classification. In: IEEE Conference on Computer Vision and Pattern Recognition. pp. 1625–1634 (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: International Conference on Learning Representations (ICLR) (2018)
2018
Cited alongside, same era.
Sharif, M., Bhagavatula, S., Bauer, L., Reiter, M.K.: A general framework for adversarial examples with objectives. ACM Transactions on Privacy and Security (TOPS) 22
2019
Closest in time.
Thys, S., Van Ranst, W., Goedemé, T.: Fooling automated surveillance cameras: adversarial patches to attack person detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)
2019
Closest in time.
Zhang, H., Wang, J.: Towards adversarially robust object detection. In: IEEE International Conference on Computer Vision. pp. 421–430 (2019)
2019
Closest in time.
Zhang, H., Shao, J., Salakhutdinov, R.: Deep neural networks with multi-branch architectures are intrinsically less non-convex. In: International Conference on Artificial Intelligence and Statistics. pp. 1099–1109 (2019)
2019
Closest in time.
Zhang, H., Yu, Y., Jiao, J., Xing, E.P., Ghaoui, L.E., Jordan, M.I.: Theoretically principled trade-off between robustness and accuracy. In: International Conference on Machine Learning (ICML) (2019)
2019
Closest in time.
Zhang, S., Wen, L., Shi, H., Lei, Z., Lyu, S., Li, S.Z.: Single-shot scale-aware network for real-time face detection. International Journal of Computer Vision 127
2019
Closest in time.
2020
Closest in time.
Brendel, W., Rauber, J., Kurakin, A., Papernot, N., Veliqi, B., Mohanty, S.P., Laurent, F., Salathé, M., Bethge, M., Yu, Y., et al.: Adversarial vision challenge. In: The NeurIPS’18 Competition, pp. 129–153 (2020)
2020
Closest in time.
Dong, Y., Fu, Q.A., Yang, X., Pang, T., Su, H., Xiao, Z., Zhu, J.: Benchmarking adversarial robustness on image classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 321–331 (2020)
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
You, S., Huang, T., Yang, M., Wang, F., Qian, C., Zhang, C.: Greedynas: Towards fast one-shot nas with greedy supernet. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1999–2008 (2020)
2020
Closest in time.