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Recent works succeeded to generate adversarial perturbations on the entire image or the object of interests to corrupt CNN based object detectors.
The PASCAL visual object classes (VOC) challenge
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Image compression using DCT and wavelet transformations
P. Telagarapu, V. J. Naveen, A. L. Prasanthi, and G. V. Santhi · 2011
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Object detectors emerge in deep scene CNNs
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2014
Earlier work this paper cites.
Fast R-CNN
R. Girshick · 2015
Earlier work this paper cites.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
SSD: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
Earlier work this paper cites.
Deepfool: a simple and accurate method to fool deep neural networks
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2016
Earlier work this paper cites.
The limitations of deep learning in adversarial settings
N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami · 2016
Cited alongside, same era.
You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
Cited alongside, same era.
T. B. Brown, D. Mané, A. Roy, M. Abadi, and J. Gilmer · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
Cited alongside, same era.
Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2017
Cited alongside, same era.
Adversarial attacks beyond the image space
X. Zeng, C. Liu, W. Qiu, L. Xie, Y.-W. Tai, C. K. Tang, and A. L. Yuille · 2017
Later among the works it cites.
Threat of adversarial attacks on deep learning in computer vision: A survey
N. Akhtar and A. Mian · 2018
Closest in time.
Learning to attack: Adversarial transformation networks
S. Baluja and I. Fischer · 2018
Closest in time.
Robust physical adversarial attack on faster r-cnn object detector
S.-T. Chen, C. Cornelius, J. Martin, and D. H. Chau · 2018
Closest in time.
Robust physical-world attacks on deep learning visual classification
I. Evtimov, K. Eykholt, E. Fernandes, T. Kohno, B. Li, A. Prakash, A. Rahmati, and D. Song · 2018
Closest in time.
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Z. Li and F. Zhou · 2017
Cited alongside, same era.
Adversarial examples that fool detectors
J. Lu, H. Sibai, and E. Fabry · 2017
Cited alongside, same era.
Standard detectors aren’t (currently) fooled by physical adversarial stop signs
J. Lu, H. Sibai, E. Fabry, and D. Forsyth · 2017
Cited alongside, same era.
Universal adversarial perturbations
S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 2017
Cited alongside, same era.
Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2017
Cited alongside, same era.
Adversarial examples for semantic segmentation and object detection
C. Xie, J. Wang, Z. Zhang, Y. Zhou, L. Xie, and A. Yuille · 2017
Cited alongside, same era.
K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, F. Tramer, A. Prakash, T. Kohno, and D. Song · 2018
Closest in time.
Lavan: Localized and visible adversarial noise
D. Karmon, D. Zoran, and Y. Goldberg · 2018
Closest in time.
Robust adversarial perturbation on deep proposal-based models
Y. Li, D. Tian, M. Chang, X. Bian, and S. Lyu · 2018
Closest in time.
Receptive field block net for accurate and fast object detection
S. Liu, D. Huang, and Y. Wang · 2018
Closest in time.
Towards imperceptible and robust adversarial example attacks against neural networks
B. Luo, Y. Liu, L. Wei, and Q. Xu · 2018
Closest in time.
Yolov3: An incremental improvement
J. Redmon and A. Farhadi · 2018
Closest in time.