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This paper demonstrates that Non-Maximum Suppression (NMS), which is commonly used in Object Detection (OD) tasks to filter redundant detection results, is no longer secure.
Y. Zhao, H. Zhu, R. Liang, Q. Shen, S. Zhang, and K. Chen, “Seeing isn’t believing: Towards more robust adversarial attack against real world object detectors,” in Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2019, pp. 1989–2004
2004
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A. Neubeck and L. Van Gool, “Efficient non-maximum suppression,” in Proceedings of the International Conference on Pattern Recognition (ICPR) , vol. 3. IEEE, 2006, pp. 850–855
2006
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2010
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C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” Computer Science , 2013
2013
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T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Proceedings of the European Conference on Computer Vision (ECCV) . Springer, 2014, pp. 740–755
2014
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R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2014, pp. 580–587
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I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” Computer Science , 2014
2014
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R. Girshick, “Fast r-cnn,” in Proceedings of the IEEE International Vonference on Computer Vision (ICCV) . IEEE, 2015, pp. 1440–1448
2015
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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 (CVPR) . IEEE, 2015, pp. 815–823
2015
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W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in Proceedings of the European Conference on Computer Vision (ECCV) . Springer, 2016, pp. 21–37
2016
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J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2016, pp. 779–788
2016
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N. K. Verma, T. Sharma, S. D. Rajurkar, and A. Salour, “Object identification for inventory management using convolutional neural network,” in 2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) . IEEE, 2016, pp. 1–6
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 (CVPR) . IEEE, 2016, pp. 770–778
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 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2016, pp. 1528–1540
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2016
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N. Papernot, P. Mcdaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” pp. 372–387, 2016
2016
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T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) . IEEE, 2017, pp. 2980–2988
2017
Cited alongside, same era.
J. H. Hosang, R. Benenson, and B. Schiele, “Learning non-maximum suppression.” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2017, pp. 6469–6477
2017
Cited alongside, same era.
N. Bodla, B. Singh, R. Chellappa, and L. S. Davis, “Soft-nms-improving object detection with one line of code,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) . IEEE, 2017, pp. 5562–5570
2017
Cited alongside, same era.
2017
Cited alongside, same era.
W. Wu, Y. Yin, X. Wang, and D. Xu, “Face detection with different scales based on faster r-cnn,” IEEE transactions on cybernetics , vol. 49, no. 11, pp. 4017–4028, 2018
2018
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N. Akhtar and A. Mian, “Threat of adversarial attacks on deep learning in computer vision: A survey,” IEEE Access , vol. 6, pp. 14 410–14 430, 2018
2018
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D. Song, K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, F. Tramer, A. Prakash, and T. Kohno, “Physical adversarial examples for object detectors,” in Proceedings of the 12th USENIX Workshop on Offensive Technologies (WOOT 18) . USENIX Association, 2018
2018
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S.-T. Chen, C. Cornelius, J. Martin, and D. H. P. Chau, “Shapeshifter: Robust physical adversarial attack on faster r-cnn object detector,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 2018, pp. 52–68
2018
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C. Xie, J. Wang, Z. Zhang, Y. Zhou, L. Xie, and A. Yuille, “Adversarial examples for semantic segmentation and object detection,” in Proceedings of the IEEE International Vonference on Computer Vision (ICCV) . IEEE, 2017, pp. 1369–1378
2017
Cited alongside, same era.
2017
Cited alongside, same era.
K. Lu, X. An, J. Li, and H. He, “Efficient deep network for vision-based object detection in robotic applications,” Neurocomputing , vol. 245, pp. 31–45, 2017
2017
Cited alongside, same era.
J. Zhang, M. Huang, X. Jin, and X. Li, “A real-time chinese traffic sign detection algorithm based on modified yolov2,” Algorithms , vol. 10, no. 4, p. 127, 2017
2017
Cited alongside, same era.
N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in Proceedings of the IEEE Symposium on Security and Privacy (S&P) . IEEE, 2017, pp. 39–57
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Z. Yang, W. Yu, P. Liang, H. Guo, L. Xia, F. Zhang, Y. Ma, and J. Ma, “Deep transfer learning for military object recognition under small training set condition,” Neural Computing and Applications , pp. 1–10, 2018
2018
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2018
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2018
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2018
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O. Sener and V. Koltun, “Multi-task learning as multi-objective optimization,” in Advances in Neural Information Processing Systems , 2018, pp. 527–538
2018
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H. Hosseini and R. Poovendran, “Semantic adversarial examples,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR) Workshops . IEEE, 2018, pp. 1614–1619
2018
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E. Yang, T. Liu, C. Deng, and D. Tao, “Adversarial examples for hamming space search,” IEEE transactions on cybernetics , vol. PP, no. 99, pp. 1–12, 2018
2018
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S. Liu, B. Kailkhura, P.-Y. Chen, P. Ting, S. Chang, and L. Amini, “Zeroth-order stochastic variance reduction for nonconvex optimization,” in Advances in Neural Information Processing Systems , 2018, pp. 3727–3737
2018
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A. N. Bhagoji, W. He, B. Li, and D. Song, “Practical black-box attacks on deep neural networks using efficient query mechanisms,” in Proceedings of the European Conference on Computer Vision (ECCV) . Springer, 2018, pp. 158–174
2018
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2018
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S. Thys, W. Van Ranst, and T. Goedemé, “Fooling automated surveillance cameras: adversarial patches to attack person detection,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR) Workshops . IEEE, 2019, pp. 0–0
2019
Closest in time.
X. Chen, C. Li, D. Wang, S. Wen, J. Zhang, S. Nepal, Y. Xiang, and K. Ren, “Android hiv: A study of repackaging malware for evading machine-learning detection,” IEEE Transactions on Information Forensics and Security , vol. 15, pp. 987–1001, 2019
2019
Closest in time.