FADER: Fast Adversarial Example Rejection

18 October 2020

Battista Biggio

Papers citing "FADER: Fast Adversarial Example Rejection"

34 / 34 papers shown

Title
Adversarial Detection by Approximation of Ensemble Boundary T. Windeatt AAML 84 0 0 18 Nov 2022
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware? Marco Melis Michele Scalas Ambra Demontis Davide Maiorca Battista Biggio Giorgio Giacinto Fabio Roli AAML FAtt 36 28 0 04 May 2020
Deep Neural Rejection against Adversarial Examples Angelo Sotgiu Ambra Demontis Marco Melis Battista Biggio Giorgio Fumera Xiaoyi Feng Fabio Roli AAML 28 68 0 01 Oct 2019
Deep-RBF Networks Revisited: Robust Classification with Rejection P. Zadeh Reshad Hosseini S. Sra AAML OOD 28 28 0 07 Dec 2018
Neural Networks with Structural Resistance to Adversarial Attacks Luca de Alfaro AAML 22 5 0 25 Sep 2018
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models Pouya Samangouei Maya Kabkab Rama Chellappa AAML GAN 66 1,172 0 17 May 2018
Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations Alex Lamb Jonathan Binas Anirudh Goyal Dmitriy Serdyuk Sandeep Subramanian Ioannis Mitliagkas Yoshua Bengio OOD 61 43 0 07 Apr 2018
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning Nicolas Papernot Patrick McDaniel OOD AAML 81 505 0 13 Mar 2018
Explaining Black-box Android Malware Detection Marco Melis Davide Maiorca Battista Biggio Giorgio Giacinto Fabio Roli AAML FAtt 14 43 0 09 Mar 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 156 3,171 0 01 Feb 2018
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning Battista Biggio Fabio Roli AAML 85 1,401 0 08 Dec 2017
Security Evaluation of Pattern Classifiers under Attack Battista Biggio Giorgio Fumera Fabio Roli AAML 37 442 0 02 Sep 2017
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid Marco Melis Ambra Demontis Battista Biggio Gavin Brown Giorgio Fumera Fabio Roli AAML 30 98 0 23 Aug 2017
Evasion Attacks against Machine Learning at Test Time Battista Biggio Igino Corona Davide Maiorca B. Nelson Nedim Srndic Pavel Laskov Giorgio Giacinto Fabio Roli AAML 93 2,140 0 21 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 211 11,962 0 19 Jun 2017
Attention Is All You Need Ashish Vaswani Noam M. Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan Gomez Lukasz Kaiser Illia Polosukhin 3DV 422 129,831 0 12 Jun 2017
MagNet: a Two-Pronged Defense against Adversarial Examples Dongyu Meng Hao Chen AAML 28 1,205 0 25 May 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 101 1,851 0 20 May 2017
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection Ambra Demontis Marco Melis Battista Biggio Davide Maiorca Dan Arp Konrad Rieck Igino Corona Giorgio Giacinto Fabio Roli AAML 31 284 0 28 Apr 2017
SafetyNet: Detecting and Rejecting Adversarial Examples Robustly Jiajun Lu Theerasit Issaranon David A. Forsyth GAN 62 380 0 01 Apr 2017
Detecting Adversarial Samples from Artifacts Reuben Feinman Ryan R. Curtin S. Shintre Andrew B. Gardner AAML 71 892 0 01 Mar 2017
On Detecting Adversarial Perturbations J. H. Metzen Tim Genewein Volker Fischer Bastian Bischoff AAML 49 947 0 14 Feb 2017
Randomized Prediction Games for Adversarial Machine Learning Samuel Rota Buló Battista Biggio I. Pillai Marcello Pelillo Fabio Roli AAML 20 61 0 03 Sep 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 157 8,497 0 16 Aug 2016
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples Nicolas Papernot Patrick McDaniel Ian Goodfellow SILM AAML 78 1,735 0 24 May 2016
End to End Learning for Self-Driving Cars Mariusz Bojarski D. Testa Daniel Dworakowski Bernhard Firner B. Flepp ... Urs Muller Jiakai Zhang Xin Zhang Jake Zhao Karol Zieba SSL 46 4,153 0 25 Apr 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 1.3K 192,638 0 10 Dec 2015
Towards Open Set Deep Networks Abhijit Bendale Terrance Boult BDL EDL 87 1,412 0 19 Nov 2015
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks Nicolas Papernot Patrick McDaniel Xi Wu S. Jha A. Swami AAML 45 3,061 0 14 Nov 2015
Evasion and Hardening of Tree Ensemble Classifiers Alex Kantchelian J. D. Tygar A. Joseph AAML 100 206 0 25 Sep 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 158 18,922 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 159 14,831 1 21 Dec 2013
Poisoning Attacks against Support Vector Machines Battista Biggio B. Nelson Pavel Laskov AAML 80 1,580 0 27 Jun 2012
A tutorial on conformal prediction Glenn Shafer V. Vovk 210 1,122 0 21 Jun 2007