Built on

A morphable model for the synthesis of 3D faces
V. Blanz and T. Vetter · 1999
Earlier work this paper cites.
Traffic sign recognition with multi-scale convolutional networks
P. Sermanet and Y. LeCun · 2011
Earlier work this paper cites.
Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
Original
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
Earlier work this paper cites.
Facewarehouse: A 3d facial expression database for visual computing
C. Cao, Y. Weng, S. Zhou, Y. Tong, and K. Zhou · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Original
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
Earlier work this paper cites.
Towards deep neural network architectures robust to adversarial examples
Original
S. Gu and L. Rigazio · 2014
Earlier work this paper cites.
Analysis of classifiers’ robustness to adversarial perturbations
Original
A. Fawzi, O. Fawzi, and P. Frossard · 2015
Earlier work this paper cites.
Deepfool: a simple and accurate method to fool deep neural networks
Original
S. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2015
Earlier work this paper cites.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
Earlier work this paper cites.

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Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
Understanding adversarial training: Increasing local stability of neural nets through robust optimization
Original
U. Shaham, Y. Yamada, and S. Negahban · 2015
Cited alongside, same era.
Robustness of classifiers: from adversarial to random noise
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A. Fawzi, S. Moosavi-Dezfooli, and P. Frossard · 2016
Cited alongside, same era.
Adversarial examples in the physical world
Original
A. Kurakin, I. J. Goodfellow, and S. Bengio · 2016
Cited alongside, same era.
Delving into transferable adversarial examples and black-box attacks
Original
Y. Liu, X. Chen, C. Liu, and D. Song · 2016

Then

Synthesizing robust adversarial examples
Original
A. Athalye and I. Sutskever · 2017
Closest in time.
An implementation of faster rcnn with study for region sampling
Original
X. Chen and A. Gupta · 2017
Closest in time.
Robust physical-world attacks on machine learning models
Original
I. Evtimov, K. Eykholt, E. Fernandes, T. Kohno, B. Li, A. Prakash, A. Rahmati, and D. Song · 2017
Closest in time.
Countering adversarial images using input transformations
Original
C. Guo, M. Rana, M. Cisse, and L. van der Maaten · 2017
Closest in time.
Face detection with the faster r-cnn
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2017

Adversarial Examples that Fool Detectors

Lu, Jiajun, Sibai, Hussein, Fabry, Evan

Understand

An adversarial example is an example that has been adjusted to produce a wrong label when presented to a system at test time.

To date, adversarial example constructions have been demonstrated for classifiers, but not for detectors.
If adversarial examples that could fool a detector exist, they could be used to (for example) maliciously create security hazards on roads populated with smart vehicles.
In this paper, we demonstrate a construction that successfully fools two standard detectors, Faster RCNN and YOLO.

Built on

A morphable model for the synthesis of 3D faces
V. Blanz and T. Vetter · 1999
Earlier work this paper cites.
Traffic sign recognition with multi-scale convolutional networks
P. Sermanet and Y. LeCun · 2011
Earlier work this paper cites.
Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
Original
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
Earlier work this paper cites.
Facewarehouse: A 3d facial expression database for visual computing
C. Cao, Y. Weng, S. Zhou, Y. Tong, and K. Zhou · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Original
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
Earlier work this paper cites.
Towards deep neural network architectures robust to adversarial examples
Original
S. Gu and L. Rigazio · 2014
Earlier work this paper cites.
Analysis of classifiers’ robustness to adversarial perturbations
Original
A. Fawzi, O. Fawzi, and P. Frossard · 2015
Earlier work this paper cites.
Deepfool: a simple and accurate method to fool deep neural networks
Original
S. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2015
Earlier work this paper cites.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
Earlier work this paper cites.

Similar

Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
Understanding adversarial training: Increasing local stability of neural nets through robust optimization
Original
U. Shaham, Y. Yamada, and S. Negahban · 2015
Cited alongside, same era.
Robustness of classifiers: from adversarial to random noise
Original
A. Fawzi, S. Moosavi-Dezfooli, and P. Frossard · 2016
Cited alongside, same era.
Adversarial examples in the physical world
Original
A. Kurakin, I. J. Goodfellow, and S. Bengio · 2016
Cited alongside, same era.
Delving into transferable adversarial examples and black-box attacks
Original
Y. Liu, X. Chen, C. Liu, and D. Song · 2016

Then

Synthesizing robust adversarial examples
Original
A. Athalye and I. Sutskever · 2017
Closest in time.
An implementation of faster rcnn with study for region sampling
Original
X. Chen and A. Gupta · 2017
Closest in time.
Robust physical-world attacks on machine learning models
Original
I. Evtimov, K. Eykholt, E. Fernandes, T. Kohno, B. Li, A. Prakash, A. Rahmati, and D. Song · 2017
Closest in time.
Countering adversarial images using input transformations
Original
C. Guo, M. Rana, M. Cisse, and L. van der Maaten · 2017
Closest in time.
Face detection with the faster r-cnn
H. Jiang and E. Learned-Miller · 2017
Closest in time.