On the Connection Between Adversarial Robustness and Saliency Map Interpretability

10 May 2019

Christian Etmann

Sebastian Lunz

Peter Maass

Carola-Bibiane Schönlieb

AAML

FAtt

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Papers citing "On the Connection Between Adversarial Robustness and Saliency Map Interpretability"

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Title
Adversarially Pretrained Transformers may be Universally Robust In-Context Learners Soichiro Kumano Hiroshi Kera Toshihiko Yamasaki AAML 18 0 0 20 May 2025
Stability of Explainable Recommendation Sairamvinay Vijayaraghavan Prasant Mohapatra AAML 43 1 0 03 May 2024
Robust Explainable Recommendation Sairamvinay Vijayaraghavan Prasant Mohapatra AAML 45 0 0 03 May 2024
Theoretical Understanding of Learning from Adversarial Perturbations Soichiro Kumano Hiroshi Kera Toshihiko Yamasaki AAML 51 1 0 16 Feb 2024
CLIPAG: Towards Generator-Free Text-to-Image Generation Roy Ganz Michael Elad VLM 38 7 0 29 Jun 2023
Adversarial Counterfactual Visual Explanations Guillaume Jeanneret Loïc Simon F. Jurie DiffM 43 27 0 17 Mar 2023
Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts Qihan Ren Huiqi Deng Yunuo Chen Siyu Lou Quanshi Zhang BDL AAML 38 10 0 25 Feb 2023
What Makes a Good Explanation?: A Harmonized View of Properties of Explanations Zixi Chen Varshini Subhash Marton Havasi Weiwei Pan Finale Doshi-Velez XAI FAtt 44 18 0 10 Nov 2022
On the Robustness of Explanations of Deep Neural Network Models: A Survey Amlan Jyoti Karthik Balaji Ganesh Manoj Gayala Nandita Lakshmi Tunuguntla Sandesh Kamath V. Balasubramanian XAI FAtt AAML 37 4 0 09 Nov 2022
Diffusion Visual Counterfactual Explanations Maximilian Augustin Valentyn Boreiko Francesco Croce Matthias Hein DiffM BDL 32 68 0 21 Oct 2022
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks Tilman Raukur A. Ho Stephen Casper Dylan Hadfield-Menell AAML AI4CE 28 125 0 27 Jul 2022
Fooling Explanations in Text Classifiers Adam Ivankay Ivan Girardi Chiara Marchiori P. Frossard AAML 35 19 0 07 Jun 2022
How explainable are adversarially-robust CNNs? Mehdi Nourelahi Lars Kotthoff Peijie Chen Anh Totti Nguyen AAML FAtt 24 8 0 25 May 2022
Sparse Visual Counterfactual Explanations in Image Space Valentyn Boreiko Maximilian Augustin Francesco Croce Philipp Berens Matthias Hein BDL CML 37 26 0 16 May 2022
How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations? Alvin Chan Yew-Soon Ong Clement Tan AAML 24 13 0 09 May 2022
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition Tianyu Pang Min Lin Xiao Yang Junyi Zhu Shuicheng Yan 40 120 0 21 Feb 2022
Visualizing Automatic Speech Recognition -- Means for a Better Understanding? Karla Markert Romain Parracone Mykhailo Kulakov Philip Sperl Ching-yu Kao Konstantin Böttinger 21 8 0 01 Feb 2022
Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction Dongfang Li Baotian Hu Qingcai Chen Tujie Xu Jingcong Tao Yunan Zhang 37 12 0 20 Dec 2021
Robust and Information-theoretically Safe Bias Classifier against Adversarial Attacks Lijia Yu Xiao-Shan Gao AAML 23 5 0 08 Nov 2021
Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning Approach A. Sarkar Anirban Sarkar Sowrya Gali V. Balasubramanian AAML 35 7 0 30 Oct 2021
Improving the trustworthiness of image classification models by utilizing bounding-box annotations K. Dharma Chicheng Zhang 32 5 0 15 Aug 2021
Trustworthy AI: A Computational Perspective Haochen Liu Yiqi Wang Wenqi Fan Xiaorui Liu Yaxin Li Shaili Jain Yunhao Liu Anil K. Jain Jiliang Tang FaML 104 197 0 12 Jul 2021
Attack to Fool and Explain Deep Networks Naveed Akhtar M. Jalwana Bennamoun Ajmal Mian AAML 32 33 0 20 Jun 2021
Adversarial Visual Robustness by Causal Intervention Kaihua Tang Ming Tao Hanwang Zhang CML AAML 32 21 0 17 Jun 2021
Impact of Spatial Frequency Based Constraints on Adversarial Robustness Rémi Bernhard Pierre-Alain Moëllic Martial Mermillod Yannick Bourrier Romain Cohendet M. Solinas M. Reyboz AAML 30 17 0 26 Apr 2021
Robust Models Are More Interpretable Because Attributions Look Normal Zifan Wang Matt Fredrikson Anupam Datta OOD FAtt 35 25 0 20 Mar 2021
Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis Leo Schwinn A. Nguyen René Raab Leon Bungert Daniel Tenbrinck Dario Zanca Martin Burger Bjoern M. Eskofier AAML 26 15 0 24 Feb 2021
On the human-recognizability phenomenon of adversarially trained deep image classifiers Jonathan W. Helland Nathan M. VanHoudnos AAML 27 4 0 18 Dec 2020
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks Tao Bai Jinqi Luo Jun Zhao AAML 51 8 0 03 Nov 2020
Quantifying the Preferential Direction of the Model Gradient in Adversarial Training With Projected Gradient Descent Ricardo Bigolin Lanfredi Joyce D. Schroeder Tolga Tasdizen 27 11 0 10 Sep 2020
Adversarial Examples in Modern Machine Learning: A Review R. Wiyatno Anqi Xu Ousmane Amadou Dia A. D. Berker AAML 21 104 0 13 Nov 2019
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training Haichao Zhang Jianyu Wang AAML 23 230 0 24 Jul 2019
Certifiably Robust Interpretation in Deep Learning Alexander Levine Sahil Singla S. Feizi FAtt AAML 31 63 0 28 May 2019
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 353 5,849 0 08 Jul 2016