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This paper focuses on learning transferable adversarial examples specifically against defense models (models to defense adversarial attacks).
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena 60
1992
Earlier work this paper cites.
Bishop, C.M.: The bias-variance decomposition. In: Pattern recognition and machine learning, chap. 3.2, pp. 147–152. springer (2006)
2006
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR (2009)
2009
Earlier work this paper cites.
Haykin, S.S.: Finite sample-size considerations. In: Neural networks and learning machines, vol. 3, chap. 2.7, pp. 82–86. Pearson Upper Saddle River (2009)
2009
Earlier work this paper cites.
2014
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: ECCV (2014)
2014
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: ICLR (2014)
2014
Earlier work this paper cites.
Everingham, M., Eslami, S.A., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: A retrospective. IJCV 111
2015
Earlier work this paper cites.
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: ICLR (2015)
2015
Earlier work this paper cites.
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: ICML (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: NeurIPS (2015)
2015
Earlier work this paper cites.
Roth, H.R., Lu, L., Farag, A., Shin, H.C., Liu, J., Turkbey, E.B., Summers, R.M.: Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation. In: MICCAI (2015)
2015
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
2015
Earlier work this paper cites.
Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Earlier work this paper cites.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Chen, P.Y., Zhang, H., Sharma, Y., Yi, J., Hsieh, C.J.: Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: ICCV (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Hendrik Metzen, J., Chaithanya Kumar, M., Brox, T., Fischer, V.: Universal adversarial perturbations against semantic image segmentation. In: ICCV (2017)
2017
Earlier work this paper cites.
Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial examples in the physical world. In: ICLR Workshop (2017)
2017
Earlier work this paper cites.
Liu, Y., Chen, X., Liu, C., Song, D.: Delving into transferable adversarial examples and black-box attacks. In: ICLR (2017)
2017
Earlier work this paper cites.
Moosavi-Dezfooli, S.M., Fawzi, A., Fawzi, O., Frossard, P.: Universal adversarial perturbations. In: CVPR (2017)
2017
Earlier work this paper cites.
Mopuri, K.R., Garg, U., Babu, R.V.: Fast feature fool: A data independent approach to universal adversarial perturbations. In: BMVC (2017)
2017
Cited alongside, same era.
Poursaeed, O., Katsman, I., Gao, B., Belongie, S.: Generative adversarial perturbations. In: CVPR (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI (2017)
2017
Cited alongside, same era.
Akhtar, N., Liu, J., Mian, A.: Defense against universal adversarial perturbations. In: CVPR (2018)
2018
Cited alongside, same era.
Xie, C., Wang, J., Zhang, Z., Ren, Z., Yuille, A.: Mitigating adversarial effects through randomization. In: ICLR (2018)
2018
Later among the works it cites.
Zhou, W., Hou, X., Chen, Y., Tang, M., Huang, X., Gan, X., Yang, Y.: Transferable adversarial perturbations. In: ECCV (2018)
2018
Later among the works it cites.
2019
Closest in time.
Cao, Y., Xiao, C., Cyr, B., Zhou, Y., Park, W., Rampazzi, S., Chen, Q.A., Fu, K., Mao, Z.M.: Adversarial sensor attack on lidar-based perception in autonomous driving. In: ACM SIGSAC CCS (2019)
2019
Closest in time.
Dong, Y., Pang, T., Su, H., Zhu, J.: Evading defenses to transferable adversarial examples by translation-invariant attacks. In: CVPR (2019)
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Baluja, S., Fischer, I.: Learning to attack: Adversarial transformation networks. In: AAAI (2018)
2018
Cited alongside, same era.
Bhagoji, A.N., He, W., Li, B., Song, D.: Practical black-box attacks on deep neural networks using efficient query mechanisms. In: ECCV (2018)
2018
Cited alongside, same era.
Brendel, W., Rauber, J., Bethge, M.: Decision-based adversarial attacks: Reliable attacks against black-box machine learning models. In: ICLR (2018)
2018
Cited alongside, same era.
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: ECCV (2018)
2018
Cited alongside, same era.
Das, N., Shanbhogue, M., Chen, S.T., Hohman, F., Li, S., Chen, L., Kounavis, M.E., Chau, D.H.: Shield: Fast, practical defense and vaccination for deep learning using jpeg compression. In: KDD. ACM (2018)
2018
Cited alongside, same era.
Dong, Y., Liao, F., Pang, T., Su, H., Hu, X., Li, J., Zhu, J.: Boosting adversarial attacks with momentum. In: CVPR (2018)
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: CVPR (2018)
2018
Cited alongside, same era.
2019
Closest in time.
Li, Y., Zhu, Z., Zhou, Y., Xia, Y., Shen, W., Fishman, E.K., Yuille, A.L.: Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-Fine Framework and Its Adversarial Examples, pp. 69–91. Springer International Publishing, Cham (2019)
2019
Closest in time.
Liu, L., Wei, W., Chow, K.H., Loper, M., Gursoy, E., Truex, S., Wu, Y.: Deep neural network ensembles against deception: Ensemble diversity, accuracy and robustness. In: 2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). pp. 274–282. IEEE (2019)
2019
Closest in time.
Naseer, M.M., Khan, S.H., Khan, M.H., Khan, F.S., Porikli, F.: Cross-domain transferability of adversarial perturbations. In: Advances in Neural Information Processing Systems. pp. 12905–12915 (2019)
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
Xie, C., Wu, Y., Maaten, L.v.d., Yuille, A.L., He, K.: Feature denoising for improving adversarial robustness. In: CVPR (2019)
2019
Closest in time.
Xie, C., Zhang, Z., Zhou, Y., Bai, S., Wang, J., Ren, Z., Yuille, A.L.: Improving transferability of adversarial examples with input diversity. In: CVPR (2019)
2019
Closest in time.
Borkar, T., Heide, F., Karam, L.: Defending against universal attacks through selective feature regeneration. In: CVPR (2020)
2020
Closest in time.
2020
Closest in time.
Huang, L., Gao, C., Zhou, Y., Xie, C., Yuille, A.L., Zou, C., Liu, N.: Universal physical camouflage attacks on object detectors. In: CVPR (2020)
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
Li, Y., Bai, S., Zhou, Y., Xie, C., Zhang, Z., Yuille, A.: Learning transferable adversarial examples via ghost networks. In: AAAI (2020)
2020
Closest in time.
2020
Closest in time.
Shafahi, A., Najibi, M., Xu, Z., Dickerson, J., Davis, L.S., Goldstein, T.: Universal adversarial training. In: AAAI (2020)
2020
Closest in time.
Sun, Y., Wang, S., Tang, X., Hsieh, T.Y., Honavar, V.: Adversarial attacks on graph neural networks via node injections: A hierarchical reinforcement learning approach. In: Proceedings of The Web Conference 2020. pp. 673–683 (2020)
2020
Closest in time.
Tang, X., Li, Y., Sun, Y., Yao, H., Mitra, P., Wang, S.: Transferring robustness for graph neural network against poisoning attacks. In: Proceedings of the 13th International Conference on Web Search and Data Mining. pp. 600–608 (2020)
2020
Closest in time.
Xiao, C., Zhong, P., Zheng, C.: Enhancing adversarial defense by k-winners-take-all. In: ICLR (2020)
2020
Closest in time.
2020
Closest in time.
Xie, C., Yuille, A.: Intriguing properties of adversarial training at scale. In: ICLR (2020)
2020
Closest in time.
2020
Closest in time.