ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector

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Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations (2015)

2015

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Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. In: Proceedings of NIPS. pp. 91–99 (2015)

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Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: Deepfool: A simple and accurate method to fool deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2574–2582 (2016)

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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 23rd ACM SIGSAC Conference on Computer and Communications Security. pp. 1528–1540 (2016)

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Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2818–2826 (2016)

2016

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Brown, T.B., Mané, D., Roy, A., Abadi, M., Gilmer, J.: Adversarial patch. arXiv preprint arXiv:1712.09665 (2017)

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2017

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Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: Proceedings of the 38th IEEE Symposium on Security and Privacy. pp. 39–57 (2017)

2017

Cited alongside, same era.

Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Song, D., Kohno, T., Rahmati, A., Prakash, A., Tramer, F.: Note on attacking object detectors with adversarial stickers. arXiv preprint arXiv:1712.08062 (2017)

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2017

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Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., Murphy, K.: Speed/accuracy trade-offs for modern convolutional object detectors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3296–3297 (2017)

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Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial examples in the physical world. In: International Conference on Learning Representations (Workshop) (2017)

2017

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Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6517–6525 (2017)

2017

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Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., Yuille, A.L.: Adversarial examples for semantic segmentation and object detection. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1378–1387 (2017)

2017

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Akhtar, N., Mian, A.S.: Threat of adversarial attacks on deep learning in computer vision: A survey. IEEE Access 6

2018

Closest in time.

Athalye, A., Carlini, N., Wagner, D.: Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples. In: Proceedings of the 35th International Conference on Machine Learning (2018)

2018

Closest in time.