Fetching the paper…
Reading the bibliography…
SentiNet is a novel detection framework for localized universal attacks on neural networks.
Y. LeCun, Y. Bengio et al. , “Convolutional networks for images, speech, and time series,” The handbook of brain theory and neural networks , 1995
1995
Earlier work this paper cites.
G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database for studying face recognition in unconstrained environments,” University of Massachusetts, Amherst, Tech. Rep. 07-49, October 2007
2007
Earlier work this paper cites.
A. R. C. K. S. P. L. Toint, “On the convergence of derivative-free methods for unconstrained optimization,” in Approximation theory and optimization: tributes to MJD Powell. , 2007, pp. 83–108
2007
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A Large-Scale Hierarchical Image Database,” in CVPR09 , 2009
2009
Earlier work this paper cites.
X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in AISTATS , 2011
2011
Earlier work this paper cites.
Y. Bengio, “Deep learning of representations for unsupervised and transfer learning,” in Proceedings of ICML Workshop on Unsupervised and Transfer Learning , 2012, pp. 17–36
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
J. R. Uijlings, K. E. Sande, T. Gevers, and A. W. Smeulders, “Selective search for object recognition,” Int. J. Comput. Vision , vol. 104, no. 2, pp. 154–171, Sep. 2013. [Online]. Available: http://dx.doi.org/10.1007/s11263-013-0620-5
2013
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and Harnessing Adversarial Examples,” ArXiv e-prints , Dec. 2014
2014
Earlier work this paper cites.
A. Møgelmose, D. Liu, and M. M. Trivedi, “Traffic sign detection for u.s. roads: Remaining challenges and a case for tracking,” in 17th International IEEE Conference on Intelligent Transportation Systems (ITSC) , Oct 2014, pp. 1394–1399
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature , 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
O. M. Parkhi, A. Vedaldi, and A. Zisserman, “Deep face recognition,” in British Machine Vision Conference , 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
M. Goldstein and S. Uchida, “A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data,” PLOS ONE , vol. 11, no. 4, pp. 1–31, 04 2016. [Online]. Available: https://doi.org/10.1371/journal.pone.0152173
2016
Cited alongside, same era.
D. Hendrycks and K. Gimpel, “Early Methods for Detecting Adversarial Images,” ArXiv e-prints , Aug. 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2017
Later among the works it cites.
2017
Later among the works it cites.
2018
Closest in time.
2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
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 , ser. CCS ’16. New York, NY, USA: ACM, 2016, pp. 1528–1540. [Online]. Available: http://doi.acm.org/10.1145/2976749.2978392
2016
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
N. Carlini and D. Wagner, “Adversarial examples are not easily detected: Bypassing ten detection methods,” in Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security . ACM, 2017, pp. 3–14
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
N. Carlini, “Evaluation and design of robust neural network defenses,” Ph.D. dissertation, EECS Department, University of California, Berkeley, Aug 2018. [Online]. Available: http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-118.html
2018
Closest in time.
2018
Closest in time.
2018
Closest in time.
2018
Closest in time.
Y. Ji, X. Zhang, S. Ji, X. Luo, and T. Wang, “Model-reuse attacks on deep learning systems,” in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2018, pp. 349–363
2018
Closest in time.
J. Johnson, “Cnn benchmarks,” 2018. [Online]. Available: https://github.com/jcjohnson/cnn-benchmarks
2018
Closest in time.
2018
Closest in time.
Y. Liu, S. Ma, Y. Aafer, W.-C. Lee, J. Zhai, W. Wang, and X. Zhang, “Trojaning attack on neural networks,” in NDSS , 2018
2018
Closest in time.
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=rJzIBfZAb
2018
Closest in time.
——, “Yolov3: An incremental improvement,” arXiv preprint arXiv:1804.02767 , 2018
2018
Closest in time.
2018
Closest in time.
2018
Closest in time.
2018
Closest in time.
2019
Closest in time.