Built on

X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3d object detection network for autonomous driving,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 1907–1915
1915
Earlier work this paper cites.
B. Biggio, B. Nelson, and P. Laskov, “Poisoning attacks against support vector machines,” arXiv preprint arXiv:1206.6389 , 2012
Original
2012
Earlier work this paper cites.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” arXiv preprint arXiv:1312.6199 , 2013
Original
2013
Earlier work this paper cites.
B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli, “Evasion attacks against machine learning at test time,” in Joint European conference on machine learning and knowledge discovery in databases . Springer, 2013, pp. 387–402
2013
Earlier work this paper cites.
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 , 2014, pp. 580–587
2014
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv preprint arXiv:1412.6572 , 2014
Original
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.
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 European conference on computer vision . Springer, 2014, pp. 740–755
2014
Earlier work this paper cites.
R. Girshick, “Fast r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1440–1448
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in neural information processing systems , 2015, pp. 91–99
2015
Earlier work this paper cites.
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” arXiv preprint arXiv:1509.02971 , 2015
Original
2015
Earlier work this paper cites.
A. Mahendran and A. Vedaldi, “Understanding deep image representations by inverting them,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 5188–5196
2015
Earlier work this paper cites.
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 , 2016, pp. 779–788
2016
Earlier work this paper cites.
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler, and R. Urtasun, “Monocular 3d object detection for autonomous driving,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 2147–2156
2016
Earlier work this paper cites.
A. Anjum, T. Abdullah, M. Tariq, Y. Baltaci, and N. Antonopoulos, “Video stream analysis in clouds: An object detection and classification framework for high performance video analytics,” IEEE Transactions on Cloud Computing , 2016
2016
Earlier work this paper cites.

Similar

N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” in 2016 IEEE European symposium on security and privacy (EuroS&P) . IEEE, 2016, pp. 372–387
2016
Cited alongside, same era.
M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang et al. , “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316 , 2016
Original
2016
Cited alongside, same era.
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
Cited alongside, same era.
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami, “Distillation as a defense to adversarial perturbations against deep neural networks,” in 2016 IEEE Symposium on Security and Privacy (SP) . IEEE, 2016, pp. 582–597
2016

Then

J. Lu, H. Sibai, E. Fabry, and D. Forsyth, “No need to worry about adversarial examples in object detection in autonomous vehicles,” arXiv preprint arXiv:1707.03501 , 2017
Original
2017
Later among the works it cites.
A. Athalye, L. Engstrom, A. Ilyas, and K. Kwok, “Synthesizing robust adversarial examples,” arXiv preprint arXiv:1707.07397 , 2017
Original
2017
Later among the works it cites.
——, “Yolov3: An incremental improvement,” arXiv preprint arXiv:1804.02767 , 2018
Original
2018
Later among the works it cites.
A. Chakraborty, M. Alam, V. Dey, A. Chattopadhyay, and D. Mukhopadhyay, “Adversarial attacks and defences: A survey,” arXiv preprint arXiv:1810.00069 , 2018
Original
2018
Later among the works it cites.

Beyond the bibliography

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Reading the bibliography…

2020

Dynamic Adversarial Patch for Evading Object Detection Models

Hoory, Shahar, Shapira, Tzvika, Shabtai, Asaf et al.

Understand

Recent research shows that neural networks models used for computer vision (e.g., YOLO and Fast R-CNN) are vulnerable to adversarial evasion attacks.

Most of the existing real-world adversarial attacks against object detectors use an adversarial patch which is attached to the target object (e.g., a carefully crafted sticker placed on a stop sign).
This method may not be robust to changes in the camera's location relative to the target object; in addition, it may not work well when applied to nonplanar objects such as cars.
In this study, we present an innovative attack method against object detectors applied in a real-world setup that addresses some of the limitations of existing attacks.

Built on

X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3d object detection network for autonomous driving,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 1907–1915
1915
Earlier work this paper cites.
B. Biggio, B. Nelson, and P. Laskov, “Poisoning attacks against support vector machines,” arXiv preprint arXiv:1206.6389 , 2012
Original
2012
Earlier work this paper cites.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” arXiv preprint arXiv:1312.6199 , 2013
Original
2013
Earlier work this paper cites.
B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli, “Evasion attacks against machine learning at test time,” in Joint European conference on machine learning and knowledge discovery in databases . Springer, 2013, pp. 387–402
2013
Earlier work this paper cites.
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 , 2014, pp. 580–587
2014
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv preprint arXiv:1412.6572 , 2014
Original
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.
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 European conference on computer vision . Springer, 2014, pp. 740–755
2014
Earlier work this paper cites.
R. Girshick, “Fast r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1440–1448
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in neural information processing systems , 2015, pp. 91–99
2015
Earlier work this paper cites.
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” arXiv preprint arXiv:1509.02971 , 2015
Original
2015
Earlier work this paper cites.
A. Mahendran and A. Vedaldi, “Understanding deep image representations by inverting them,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 5188–5196
2015
Earlier work this paper cites.
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 , 2016, pp. 779–788
2016
Earlier work this paper cites.
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler, and R. Urtasun, “Monocular 3d object detection for autonomous driving,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 2147–2156
2016
Earlier work this paper cites.
A. Anjum, T. Abdullah, M. Tariq, Y. Baltaci, and N. Antonopoulos, “Video stream analysis in clouds: An object detection and classification framework for high performance video analytics,” IEEE Transactions on Cloud Computing , 2016
2016
Earlier work this paper cites.

Similar

N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” in 2016 IEEE European symposium on security and privacy (EuroS&P) . IEEE, 2016, pp. 372–387
2016
Cited alongside, same era.
M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang et al. , “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316 , 2016
Original
2016
Cited alongside, same era.
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
Cited alongside, same era.
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami, “Distillation as a defense to adversarial perturbations against deep neural networks,” in 2016 IEEE Symposium on Security and Privacy (SP) . IEEE, 2016, pp. 582–597
2016

Then

J. Lu, H. Sibai, E. Fabry, and D. Forsyth, “No need to worry about adversarial examples in object detection in autonomous vehicles,” arXiv preprint arXiv:1707.03501 , 2017
Original
2017
Later among the works it cites.
A. Athalye, L. Engstrom, A. Ilyas, and K. Kwok, “Synthesizing robust adversarial examples,” arXiv preprint arXiv:1707.07397 , 2017
Original
2017
Later among the works it cites.
——, “Yolov3: An incremental improvement,” arXiv preprint arXiv:1804.02767 , 2018
Original
2018
Later among the works it cites.
A. Chakraborty, M. Alam, V. Dey, A. Chattopadhyay, and D. Mukhopadhyay, “Adversarial attacks and defences: A survey,” arXiv preprint arXiv:1810.00069 , 2018
Original
2018
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Cited alongside, same era.

A. Kurakin, I. Goodfellow, and S. Bengio, “Adversarial examples in the physical world,” arXiv preprint arXiv:1607.02533 , 2016

Original

2016

Cited alongside, same era.

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 , 2016, pp. 1528–1540

2016

Cited alongside, same era.

J. Redmon and A. Farhadi, “Yolo9000: better, faster, stronger,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 7263–7271

2017

Cited alongside, same era.

K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2961–2969

2017

Cited alongside, same era.

B. Wu, F. Iandola, P. H. Jin, and K. Keutzer, “Squeezedet: Unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2017, pp. 129–137

2017

Cited alongside, same era.

G. Ning, Z. Zhang, C. Huang, X. Ren, H. Wang, C. Cai, and Z. He, “Spatially supervised recurrent convolutional neural networks for visual object tracking,” in 2017 IEEE International Symposium on Circuits and Systems (ISCAS) . IEEE, 2017, pp. 1–4

2017

Cited alongside, same era.

N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in 2017 ieee symposium on security and privacy (sp) . IEEE, 2017, pp. 39–57

2017

Cited alongside, same era.

S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard, “Universal adversarial perturbations,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1765–1773

2017

Cited alongside, same era.

Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 4490–4499

2018