Explaining and harnessing adversarial examples, 2014. ,
The limitations of deep learning in adversarial settings, Proc. IEEE Eur. Symp. Secur. Privacy (EuroS&P), pp.372-387, 2016. ,
Houdini: Fooling deep structured prediction models, 2017. ,
Is deep learning safe for robot vision? Adversarial examples against the iCub humanoid, Proc. IEEE Int. Conf. Comput. Vis. (CVPR), pp.751-759, 2017. ,
Delving into transferable adversarial examples and black-box attacks, 2016. ,
Perceptual evaluation of adversarial attacks for CNN-based image classification, Proc. 11th IEEE Int. Conf. Qual. Multimedia Exper. (QoMEX), pp.1-6, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02302604
Transferability in machine learning: From phenomena to black-box attacks using adversarial samples, 2016. ,
La cryptographie militaire, J. des Sci. Militaires, vol.9, pp.5-38, 1883. ,
Communication theory of secrecy systems, Bell Labs Tech. J, vol.28, issue.4, pp.656-715, 1949. ,
Countering adversarial images using input transformations, 2017. ,
Defense against adversarial attacks using high-level representation guided denoiser, Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.1778-1787, 2018. ,
Towards evaluating the robustness of neural networks, Proc. IEEE Symp. Secur. Privacy (SP), pp.39-57, 2017. ,
Intriguing properties of neural networks, 2013. ,
Adversarial examples: Attacks and defenses for deep learning, IEEE Trans. Neural Netw. Learn. Syst, vol.30, issue.9, pp.2805-2824, 2019. ,
Threat of adversarial attacks on deep learning in computer vision: A survey, IEEE Access, vol.6, pp.14410-14430, 2018. ,
Review of artificial intelligence adversarial attack and defense technologies, Appl. Sci, vol.9, issue.5, p.909, 2019. ,
Boosting adversarial attacks with momentum, Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.9185-9193, 2018. ,
Towards deep learning models resistant to adversarial attacks, 2017. ,
Ensemble adversarial training: Attacks and defenses, 2017. ,
DeepFool: A simple and accurate method to fool deep neural networks, Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp.2574-2582, 2016. ,
Distillation as a defense to adversarial perturbations against deep neural networks,'' in Proc, IEEE Symp. Secur. Privacy (SP), pp.582-597, 2016. ,
Distilling the knowledge in a neural network, 2015. ,
MagNet: A two-pronged defense against adversarial examples, Proc. ACM SIGSAC Conf. Comput. Commun. Secur, pp.135-147, 2017. ,
Generalized denoising autoencoders as generative models,'' in Proc, Adv. Neural Inf. Process. Syst, pp.899-907, 2013. ,
Provably minimallydistorted adversarial examples, 2018. ,
Gradient-based learning applied to document recognition, Proc. IEEE, vol.86, pp.2278-2324, 1998. ,
Learning multiple layers of features from tiny images, 2009. ,
Technical report on the CleverHans v2.1.0 adversarial examples library, 2016. ,
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. ,
Defense-GAN: Protecting classifiers against adversarial attacks using generative models, 2018. ,
On evaluating adversarial robustness, 2019. ,
Practical black-box attacks against machine learning, Proc. ACM Asia Conf. Comput. Commun. Secur, pp.506-519, 2017. ,
Robustifying models against adversarial attacks by Langevin dynamics, 2018. ,
Defense against adversarial attacks with saak transform, 2018. ,