The Odds are Odd: A Statistical Test for Detecting Adversarial Examples

13 February 2019

Papers citing "The Odds are Odd: A Statistical Test for Detecting Adversarial Examples"

33 / 33 papers shown

Title
CaFA: Cost-aware, Feasible Attacks With Database Constraints Against Neural Tabular Classifiers Matan Ben-Tov Daniel Deutch Nave Frost Mahmood Sharif AAML 107 0 0 20 Jan 2025
Adversarial Detection with a Dynamically Stable System Xiaowei Long Jie Lin Xiangyuan Yang AAML 36 0 0 11 Nov 2024
Trustworthy Actionable Perturbations Jesse Friedbaum S. Adiga Ravi Tandon AAML 38 2 0 18 May 2024
A LLM Assisted Exploitation of AI-Guardian Nicholas Carlini ELM SILM 24 15 0 20 Jul 2023
Graph-based methods coupled with specific distributional distances for adversarial attack detection dwight nwaigwe Lucrezia Carboni Martial Mermillod Sophie Achard M. Dojat AAML 24 3 0 31 May 2023
Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking Ruixiang Tang Qizhang Feng Ninghao Liu Fan Yang Xia Hu 24 36 0 20 Mar 2023
Explainability and Robustness of Deep Visual Classification Models Jindong Gu AAML 39 2 0 03 Jan 2023
ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation Fan Yin Yao Li Cho-Jui Hsieh Kai-Wei Chang AAML 60 4 0 22 Oct 2022
Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization Ziquan Liu Antoni B. Chan AAML 25 5 0 11 Oct 2022
DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning Mohammad Hossein Samavatian Saikat Majumdar Kristin Barber R. Teodorescu AAML 14 2 0 31 Jul 2022
Increasing Confidence in Adversarial Robustness Evaluations Roland S. Zimmermann Wieland Brendel Florian Tramèr Nicholas Carlini AAML 36 16 0 28 Jun 2022
On the Limitations of Stochastic Pre-processing Defenses Yue Gao Ilia Shumailov Kassem Fawaz Nicolas Papernot AAML SILM 36 30 0 19 Jun 2022
Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINO Javier Rando Nasib Naimi Thomas Baumann Max Mathys AAML 18 5 0 14 Jun 2022
Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems Shail Dave Alberto Marchisio Muhammad Abdullah Hanif Amira Guesmi Aviral Shrivastava Ihsen Alouani Muhammad Shafique 28 13 0 18 Apr 2022
"That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks Edoardo Mosca Shreyash Agarwal Javier Rando Georg Groh AAML 25 30 0 10 Apr 2022
Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses Chun Pong Lau Jiang-Long Liu Hossein Souri Wei-An Lin S. Feizi Ramalingam Chellappa AAML 29 12 0 12 Dec 2021
Adaptive Perturbation for Adversarial Attack Zheng Yuan Jie M. Zhang Zhaoyan Jiang Liangliang Li Shiguang Shan AAML 21 3 0 27 Nov 2021
SoK: Machine Learning Governance Varun Chandrasekaran Hengrui Jia Anvith Thudi Adelin Travers Mohammad Yaghini Nicolas Papernot 32 16 0 20 Sep 2021
Simple Post-Training Robustness Using Test Time Augmentations and Random Forest Gilad Cohen Raja Giryes AAML 29 4 0 16 Sep 2021
Advances in adversarial attacks and defenses in computer vision: A survey Naveed Akhtar Ajmal Saeed Mian Navid Kardan M. Shah AAML 26 235 0 01 Aug 2021
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them Florian Tramèr AAML 16 64 0 24 Jul 2021
BAARD: Blocking Adversarial Examples by Testing for Applicability, Reliability and Decidability Luke Chang Katharina Dost Kaiqi Zhao Ambra Demontis Fabio Roli Gillian Dobbie Jörg Simon Wicker AAML 19 2 0 02 May 2021
Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS Felix O. Olowononi D. Rawat Chunmei Liu 29 131 0 14 Feb 2021
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks Wei-An Lin Chun Pong Lau Alexander Levine Ramalingam Chellappa S. Feizi AAML 78 60 0 05 Sep 2020
Towards Feature Space Adversarial Attack Qiuling Xu Guanhong Tao Shuyang Cheng X. Zhang GAN AAML 25 25 0 26 Apr 2020
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel A. Madry AAML 80 820 0 19 Feb 2020
Deflecting Adversarial Attacks Yao Qin Nicholas Frosst Colin Raffel G. Cottrell Geoffrey E. Hinton AAML 20 15 0 18 Feb 2020
Analysis of Random Perturbations for Robust Convolutional Neural Networks Adam Dziedzic S. Krishnan OOD AAML 16 1 0 08 Feb 2020
When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures Gil Fidel Ron Bitton A. Shabtai FAtt GAN 13 119 0 08 Sep 2019
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions Yao Qin Nicholas Frosst S. Sabour Colin Raffel G. Cottrell Geoffrey E. Hinton GAN AAML 17 71 0 05 Jul 2019
Defending Adversarial Attacks by Correcting logits Yifeng Li Lingxi Xie Ya-Qin Zhang Rui Zhang Yanfeng Wang Qi Tian AAML 29 5 0 26 Jun 2019
DeepFense: Online Accelerated Defense Against Adversarial Deep Learning B. Rouhani Mohammad Samragh Mojan Javaheripi T. Javidi F. Koushanfar AAML 10 15 0 08 Sep 2017
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 281 5,835 0 08 Jul 2016