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Image compression-based approaches for defending against the adversarial-example attacks, which threaten the safety use of deep neural networks (DNN), have been investigated recently.
Distributions of the two-dimensional dct coefficients for images
R. Reininger and J. Gibson · 1983
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The jpeg still picture compression standard
G. K. Wallace · 1992
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Detecting digital image forgeries by measuring inconsistencies of blocking artifact
S. Ye, Q. Sun, and E.-C. Chang · 2007
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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A study of the effect of jpg compression on adversarial images
G. K. Dziugaite, Z. Ghahramani, and D. M. Roy · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
S. M. Moosavi Dezfooli, A. Fawzi, and P. Frossard · 2016
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Looking at humans in the age of self-driving and highly automated vehicles
E. Ohn-Bar and M. M. Trivedi · 2016
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Dimensionality reduction as a defense against evasion attacks on machine learning classifiers
A. N. Bhagoji, D. Cullina, and P. Mittal · 2017
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
Earlier work this paper cites.
Keeping the bad guys out: Protecting and vaccinating deep learning with jpeg compression
N. Das, M. Shanbhogue, S.-T. Chen, F. Hohman, L. Chen, M. E. Kounavis, and D. H. Chau · 2017
Cited alongside, same era.
Countering adversarial images using input transformations
C. Guo, M. Rana, M. Cissé, and L. van der Maaten · 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.
Toward low-flying autonomous mav trail navigation using deep neural networks for environmental awareness
N. Smolyanskiy, A. Kamenev, J. Smith, and S. Birchfield · 2017
Cited alongside, same era.
Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
The effects of jpeg and jpeg2000 compression on attacks using adversarial examples
A. E. Aydemir, A. Temizel, and T. T. Temizel · 2018
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Shield: Fast, practical defense and vaccination for deep learning using jpeg compression
N. Das, M. Shanbhogue, S.-T. Chen, F. Hohman, S. Li, L. Chen, M. E. Kounavis, and D. H. Chau · 2018
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Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2018
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Defense against adversarial attacks using high-level representation guided denoiser
F. Liao, M. Liang, Y. Dong, T. Pang, J. Zhu, and X. Hu · 2018
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Security analysis and enhancement of model compressed deep learning systems under adversarial attacks
Q. Liu, T. Liu, Z. Liu, Y. Wang, Y. Jin, and W. Wen · 2018
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Y. Song, T. Kim, S. Nowozin, S. Ermon, and N. Kushman · 2017
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
F. Tramèr, A. Kurakin, N. Papernot, I. Goodfellow, D. Boneh, and P. M · 2017
Cited alongside, same era.
Mitigating adversarial effects through randomization
C. Xie, J. Wang, Z. Zhang, Z. Ren, and A. Yuille · 2017
Cited alongside, same era.
Just-noticeable difference-based perceptual optimization for jpeg compression
X. Zhang, S. Wang, K. Gu, W. Lin, S. Ma, and W. Gao · 2017
Cited alongside, same era.
Threat of adversarial attacks on deep learning in computer vision: A survey
N. Akhtar and A. Mian · 2018
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
A. Athalye, N. Carlini, and D. Wagner · 2018
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning
M. Abadi, P. Barham, et al
Cited in the paper.
A. Prakash, N. Moran, S. Garber, A. DiLillo, and J. Storer · 2018
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Defense-gan: Protecting classifiers against adversarial attacks using generative models
P. Samangouei, M. Kabkab, and R. Chellappa · 2018
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Defending against adversarial images using basis functions transformations
U. Shaham, J. Garritano, Y. Yamada, E. Weinberger, A. Cloninger, X. Cheng, K. Stanton, and Y. Kluger · 2018
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Adversarial risk and the dangers of evaluating against weak attacks
J. Uesato, B. O’Donoghue, A. v. d. Oord, and P. Kohli · 2018
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Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
W. Xu, D. Evans, and Y. Qi · 2018
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