Adversarial Robustness on In- and Out-Distribution Improves Explainability

20 March 2020

Matthias Hein

Papers citing "Adversarial Robustness on In- and Out-Distribution Improves Explainability"

23 / 23 papers shown

Title
Diffusion-based Adversarial Purification from the Perspective of the Frequency Domain Gaozheng Pei Ke Ma Yingfei Sun Qianqian Xu Q. Huang DiffM 40 0 0 02 May 2025
HALO: Robust Out-of-Distribution Detection via Joint Optimisation Hugo Lyons Keenan S. Erfani Christopher Leckie OODD 204 0 0 27 Feb 2025
MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework Ping Guo Cheng Gong Xi Victoria Lin Fei Liu Zhichao Lu Qingfu Zhang Zhenkun Wang AAML 45 0 0 13 Jan 2025
Theoretical Understanding of Learning from Adversarial Perturbations Soichiro Kumano Hiroshi Kera Toshihiko Yamasaki AAML 31 1 0 16 Feb 2024
Enhancing Adversarial Robustness via Score-Based Optimization Boya Zhang Weijian Luo Zhihua Zhang DiffM 24 12 0 10 Jul 2023
Group-based Robustness: A General Framework for Customized Robustness in the Real World Weiran Lin Keane Lucas Neo Eyal Lujo Bauer Michael K. Reiter Mahmood Sharif OOD AAML 22 1 0 29 Jun 2023
Function Composition in Trustworthy Machine Learning: Implementation Choices, Insights, and Questions Manish Nagireddy Moninder Singh Samuel C. Hoffman Evaline Ju K. Ramamurthy Kush R. Varshney 22 1 0 17 Feb 2023
Diffusion Visual Counterfactual Explanations Maximilian Augustin Valentyn Boreiko Francesco Croce Matthias Hein DiffM BDL 32 68 0 21 Oct 2022
How many perturbations break this model? Evaluating robustness beyond adversarial accuracy R. Olivier Bhiksha Raj AAML 29 5 0 08 Jul 2022
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities Julian Bitterwolf Alexander Meinke Maximilian Augustin Matthias Hein OODD 13 25 0 20 Jun 2022
Sparse Visual Counterfactual Explanations in Image Space Valentyn Boreiko Maximilian Augustin Francesco Croce Philipp Berens Matthias Hein BDL CML 30 26 0 16 May 2022
Diffusion Models for Adversarial Purification Weili Nie Brandon Guo Yujia Huang Chaowei Xiao Arash Vahdat Anima Anandkumar WIGM 195 418 0 16 May 2022
Boundary Defense Against Black-box Adversarial Attacks Manjushree B. Aithal Xiaohua Li AAML 17 6 0 31 Jan 2022
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks Maksym Yatsura J. H. Metzen Matthias Hein OOD 24 14 0 02 Nov 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
Dynamic Neural Network Architectural and Topological Adaptation and Related Methods -- A Survey Lorenz Kummer AI4CE 32 0 0 28 Jul 2021
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity Shiwei Liu Tianlong Chen Zahra Atashgahi Xiaohan Chen Ghada Sokar Elena Mocanu Mykola Pechenizkiy Zhangyang Wang D. Mocanu OOD 28 49 0 28 Jun 2021
Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation Agnieszka Słowik Léon Bottou FaML 37 19 0 17 Jun 2021
Learning Energy-Based Models With Adversarial Training Xuwang Yin Shiying Li Gustavo K. Rohde AAML DiffM 30 9 0 11 Dec 2020
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples Sven Gowal Chongli Qin J. Uesato Timothy A. Mann Pushmeet Kohli AAML 17 323 0 07 Oct 2020
Explaining Visual Models by Causal Attribution Álvaro Parafita Jordi Vitrià CML FAtt 62 35 0 19 Sep 2019
Disentangling Adversarial Robustness and Generalization David Stutz Matthias Hein Bernt Schiele AAML OOD 186 272 0 03 Dec 2018
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 226 1,835 0 03 Feb 2017