Touvron H. Cord M. Douze M. Massa F. Sablayrolles A. Jégou H. Training data-efficient image transformers & distillation through attention arXiv 2021 2012.12877
Chakraborty A. Alam M. Dey V. Chattopadhyay A. Mukhopadhyay D. Adversarial Attacks and Defences: A Survey arXiv 2018 1810.00069 10.1049/cit2.12028
Kurakin A. Goodfellow I.J. Bengio S. Adversarial examples in the physical world arXiv 2016 1607.02533
Goodfellow I.J. Shlens J. Szegedy C. Explaining and Harnessing Adversarial Examples arXiv 2015 1810.00069
Carlini N. Wagner D.A. Towards Evaluating the Robustness of Neural Networks arXiv 2016 1810.00069
Wiyatno R. Xu A. Maximal Jacobian-based Saliency Map Attack arXiv 2018 1808.07945
Tsipras D. Santurkar S. Engstrom L. Turner A. Madry A. Robustness may be at odds with accuracy arXiv 2018 1805.12152
Tramèr F. Papernot N. Goodfellow I. Boneh D. McDaniel P. The Space of Transferable Adversarial Examples arXiv 2017 1704.03453
Liu Y. Chen X. Liu C. Song D. Delving into Transferable Adversarial Examples and Black-box Attacks arXiv 2016 1611.02770
Ilyas A. Engstrom L. Athalye A. Lin J. Black-box Adversarial Attacks with Limited Queries and Information arXiv 2018 1804.08598
Narodytska N. Kasiviswanathan S.P. Simple Black-Box Adversarial Perturbations for Deep Networks arXiv 2016 1612.06299
Andriushchenko M. Croce F. Flammarion N. Hein M. Square attack: A query-efficient black-box adversarial attack via random search European Conference on Computer Vision Springer Cham, Switzerland 2020 484 501
Sinha S. Garg A. Larochelle H. Curriculum by Texture arXiv 2020 2003.01367
Topal A.O. Chitic R. Leprévost F. One Evolutionary Algorithm Deceives Humans and Ten Convolutional Neural Networks Trained on ImageNet at Image Recognition 2022 submitted
Bernard N. Leprévost F. Evolutionary Algorithms for Convolutional Neural Network Visualisation Proceedings of the High Performance Computing–5th Latin American Conference, CARLA 2018 Bucaramanga, Colombia 23–28 September 2018 Communications in Computer and Information Science Springer Berlin/Heidelberg, Germany 2018 Volume 979 18 32
Chitic R. Bernard N. Leprévost F. A proof of concept to deceive humans and machines at image classification with evolutionary algorithms Proceedings of the 12th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2020 Phuket, Thailand 23–26 March 2020 Springer Berlin/Heidelberg, Germany 2020 467 480
Chitic R. Leprévost F. Bernard N. Evolutionary algorithms deceive humans and machines at image classification: An extended proof of concept on two scenarios J. Inf. Telecommun. 2020 5 121 143 10.1080/24751839.2020.1829388
Jung J. Akhtar N. Hassan G.M. Analysing Adversarial Examples for Deep Learning Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications Vienna, Austria 8–10 February 2021
Wang Z. Yang Y. Shrivastava A. Rawal V. Ding Z. Towards Frequency-Based Explanation for Robust CNN arXiv 2020 2005.03141
Yin D. Lopes R.G. Shlens J. Cubuk E.D. Gilmer J. A Fourier Perspective on Model Robustness in Computer Vision arXiv 2019 1906.08988
Geirhos R. Rubisch P. Michaelis C. Bethge M. Wichmann F. Brendel W. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness arXiv 2018 1811.12231
Zhang T. Zhu Z. Interpreting Adversarially Trained Convolutional Neural Networks arXiv 2019 1905.09797
Huang Q. Katsman I. Gu Z. He H. Belongie S. Lim S.N. Enhancing Adversarial Example Transferability With an Intermediate Level Attack Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV) Seoul, Korea 27 October–2 November 2019
Islam M.A. Kowal M. Esser P. Jia S. Ommer B. Derpanis K.G. Bruce N.D.B. Shape or Texture: Understanding Discriminative Features in CNNs arXiv 2021 2101.11604
Cantareira G.D. de Mello R.F. Paulovich F.V. Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles arXiv 2021 2103.10229
Deng J. Dong W. Socher R. Li L.J. Li K. Li F.F. The ImageNet Image Database 2009 Available online: http://image-net.org (accessed on 10 February 2022)
Paszke A. Gross S. Massa F. Lerer A. Bradbury J. Chanan G. Killeen T. Lin Z. Gimelshein N. Antiga L. et al. Pytorch: An imperative style, high-performance deep learning library arXiv 2019 1912.01703
Huang G. Liu Z. Van Der Maaten L. Weinberger K.Q. Densely connected convolutional networks Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) Honolulu, HI, USA 21–26 July 2017 4700 4708
Howard A.G. Zhu M. Chen B. Kalenichenko D. Wang W. Weyand T. Andreetto M. Adam H. Mobilenets: Efficient convolutional neural networks for mobile vision applications arXiv 2017 1704.04861
Tan M. Chen B. Pang R. Vasudevan V. Le Q.V. MnasNet: Platform-Aware Neural Architecture Search for Mobile arXiv 2018 1807.11626
He K. Zhang X. Ren S. Sun J. Deep residual learning for image recognition Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) Las Vegas, NV, USA 27–30 June 2016 770 778
Simonyan K. Zisserman A. Very deep convolutional networks for large-scale image recognition arXiv 2014 1409.1556
Brendel W. Bethge M. Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet arXiv 2019 1904.00760
Van Rossum G. Drake F.L. Python 3 Reference Manual CreateSpace Scotts Valley, CA, USA 2009
Varrette S. Bouvry P. Cartiaux H. Georgatos F. Management of an Academic HPC Cluster: The UL Experience Proceedings of the 2014 International Conference on High Performance Computing & Simulation (HPCS 2014) Bologna, Italy 21–25 July 2014 959 967
Luo W. Li Y. Urtasun R. Zemel R.S. Understanding the Effective Receptive Field in Deep Convolutional Neural Networks arXiv 2017 1701.04128
Lenc K. Vedaldi A. Understanding image representations by measuring their equivariance and equivalence arXiv 2014 1411.5908
Morcosa A.S. Raghu M. Bengio S. Insights on representational similarity in neural networks with canonical correlation arXiv 2018 1806.05759
Fawzi A. Moosavi-Dezfooli S.M. Frossard P. Robustness of classifiers: From adversarial to random noise arXiv 2016 1608.08967
