Fetching the paper…
Reading the bibliography…
Recent studies have shown that state-of-the-art deep learning models are vulnerable to the inputs with small perturbations (adversarial examples).
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber · 2006
Earlier work this paper cites.
End-to-end scene text recognition
K. Wang, B. Babenko, and S. Belongie · 2011
Earlier work this paper cites.
Scene text recognition using higher order language priors
A. Mishra, K. Alahari, and C. V. Jawahar · 2012
Earlier work this paper cites.
Icdar 2013 robust reading competition
D. Karatzas, F. Shafait, and S. e. a. Uchida · 2013
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 · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
I. Goodfellow, J. Shlens, and C. Szegedy · 2015
Earlier work this paper cites.
Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
N. Papernot, P. D. McDaniel, and I. J. Goodfellow · 2016
Earlier work this paper cites.
Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
M. Sharif, S. Bhagavatula, L. Bauer, and M. K. Reiter · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
Cited alongside, same era.
Adversarial examples are not easily detected: Bypassing ten detection methods
N. Carlini and D. Wagner · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
N. Carlini and D. Wagner · 2017
Cited alongside, same era.
Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
P.-Y. Chen, H. Zhang, Y. Sharma, J. Yi, and C.-J. Hsieh · 2017
Cited alongside, same era.
Universal adversarial perturbations against semantic image segmentation
J. Hendrik Metzen, M. Chaithanya Kumar, T. Brox, and V. Fischer · 2017
Cited alongside, same era.
Adversarial examples for evaluating reading comprehension systems
R. Jia and P. Liang · 2017
Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2017
Later among the works it cites.
An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition
B. Shi, X. Bai, and C. Yao · 2017
Later among the works it cites.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
A. Athalye, N. Carlini, and D. Wagner · 2018
Closest in time.
Audio adversarial examples: Targeted attacks on speech-to-text
N. Carlini and D. Wagner · 2018
Closest in time.
Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
A. Kendall, Y. Gal, and R. Cipolla · 2018
Closest in time.
Cascade adversarial machine learning regularized with a unified embedding
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
What uncertainties do we need in bayesian deep learning for computer vision?
A. Kendall and Y. Gal · 2017
Cited alongside, same era.
Delving into adversarial attacks on deep policies
J. Kos and D. Song · 2017
Cited alongside, same era.
T. Na, J. H. Ko, and S. Mukhopadhyay · 2018
Closest in time.
Adversarial examples: Attacks and defenses for deep learning
X. Yuan, P. He, Q. Zhu, and X. Li · 2019
Closest in time.