On the Robustness of Explanations of Deep Neural Network Models: A Survey

9 November 2022

Amlan Jyoti

Karthik Balaji Ganesh

Manoj Gayala

Nandita Lakshmi Tunuguntla

Papers citing "On the Robustness of Explanations of Deep Neural Network Models: A Survey"

50 / 90 papers shown

Title
A Survey on Physical Adversarial Attack in Computer Vision Donghua Wang Wen Yao Tingsong Jiang Guijian Tang Xiaoqian Chen AAML 113 39 0 28 Sep 2022
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective Mark Huasong Meng Guangdong Bai Sin Gee Teo Zhe Hou Yan Xiao Yun Lin Jin Song Dong AAML 68 44 0 24 Jun 2022
Fooling Explanations in Text Classifiers Adam Ivankay Ivan Girardi Chiara Marchiori P. Frossard AAML 66 19 0 07 Jun 2022
How explainable are adversarially-robust CNNs? Mehdi Nourelahi Lars Kotthoff Peijie Chen Anh Totti Nguyen AAML FAtt 59 8 0 25 May 2022
Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection Fan Wang A. Kong 155 10 0 15 May 2022
Rethinking Stability for Attribution-based Explanations Chirag Agarwal Nari Johnson Martin Pawelczyk Satyapriya Krishna Eshika Saxena Marinka Zitnik Himabindu Lakkaraju FAtt 71 53 0 14 Mar 2022
A Survey of Adversarial Defences and Robustness in NLP Shreyansh Goyal Sumanth Doddapaneni Mitesh M.Khapra B. Ravindran AAML 77 30 0 12 Mar 2022
Saliency Diversified Deep Ensemble for Robustness to Adversaries Alexander A. Bogun Dimche Kostadinov Damian Borth AAML FedML 33 5 0 07 Dec 2021
Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing Sanchit Sinha Hanjie Chen Arshdeep Sekhon Yangfeng Ji Yanjun Qi AAML FAtt 60 41 0 11 Aug 2021
Advances in adversarial attacks and defenses in computer vision: A survey Naveed Akhtar Ajmal Mian Navid Kardan M. Shah AAML 88 240 0 01 Aug 2021
Certifiably Robust Interpretation via Renyi Differential Privacy Ao Liu Xiaoyu Chen Sijia Liu Lirong Xia Chuang Gan AAML 47 14 0 04 Jul 2021
Certification of embedded systems based on Machine Learning: A survey Guillaume Vidot Christophe Gabreau I. Ober Iulian Ober 39 12 0 14 Jun 2021
Fooling Partial Dependence via Data Poisoning Hubert Baniecki Wojciech Kretowicz P. Biecek AAML 51 23 0 26 May 2021
A Review on Explainability in Multimodal Deep Neural Nets Gargi Joshi Rahee Walambe K. Kotecha 113 141 0 17 May 2021
Improving Attribution Methods by Learning Submodular Functions Piyushi Manupriya Tarun Ram Menta S. Jagarlapudi V. Balasubramanian TDI 56 6 0 19 Apr 2021
Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey Thomas Rojat Raphael Puget David Filliat Javier Del Ser R. Gelin Natalia Díaz Rodríguez XAI AI4TS 86 135 0 02 Apr 2021
Robust Models Are More Interpretable Because Attributions Look Normal Zifan Wang Matt Fredrikson Anupam Datta OOD FAtt 64 26 0 20 Mar 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 218 672 0 20 Mar 2021
Evaluating Robustness of Counterfactual Explanations André Artelt Valerie Vaquet Riza Velioglu Fabian Hinder Johannes Brinkrolf M. Schilling Barbara Hammer 91 46 0 03 Mar 2021
A Survey On Universal Adversarial Attack Chaoning Zhang Philipp Benz Chenguo Lin Adil Karjauv Jing Wu In So Kweon AAML 60 93 0 02 Mar 2021
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations Sushant Agarwal S. Jabbari Chirag Agarwal Sohini Upadhyay Zhiwei Steven Wu Himabindu Lakkaraju FAtt AAML 66 63 0 21 Feb 2021
Explainable Artificial Intelligence Approaches: A Survey Sheikh Rabiul Islam W. Eberle S. Ghafoor Mohiuddin Ahmed XAI 73 103 0 23 Jan 2021
Enhanced Regularizers for Attributional Robustness A. Sarkar Anirban Sarkar V. Balasubramanian 41 16 0 28 Dec 2020
Towards Robust Explanations for Deep Neural Networks Ann-Kathrin Dombrowski Christopher J. Anders K. Müller Pan Kessel FAtt 75 63 0 18 Dec 2020
A Survey on the Explainability of Supervised Machine Learning Nadia Burkart Marco F. Huber FaML XAI 53 774 0 16 Nov 2020
FAR: A General Framework for Attributional Robustness Adam Ivankay Ivan Girardi Chiara Marchiori P. Frossard OOD 71 22 0 14 Oct 2020
How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks Thomas Fel David Vigouroux Rémi Cadène Thomas Serre XAI FAtt 65 31 0 07 Sep 2020
Adversarial Examples on Object Recognition: A Comprehensive Survey A. Serban E. Poll Joost Visser AAML 94 73 0 07 Aug 2020
Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging N. Arun N. Gaw P. Singh Ken Chang M. Aggarwal ... J. Patel M. Gidwani Julius Adebayo M. D. Li Jayashree Kalpathy-Cramer FAtt 79 110 0 06 Aug 2020
Adversarial Attacks against Face Recognition: A Comprehensive Study Fatemeh Vakhshiteh A. Nickabadi Raghavendra Ramachandra AAML 55 16 0 22 Jul 2020
A simple defense against adversarial attacks on heatmap explanations Laura Rieger Lars Kai Hansen FAtt AAML 76 37 0 13 Jul 2020
Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey S. Silva Peyman Najafirad AAML OOD 56 134 0 01 Jul 2020
Proper Network Interpretability Helps Adversarial Robustness in Classification Akhilan Boopathy Sijia Liu Gaoyuan Zhang Cynthia Liu Pin-Yu Chen Shiyu Chang Luca Daniel AAML FAtt 116 66 0 26 Jun 2020
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey Arun Das P. Rad XAI 155 603 0 16 Jun 2020
On Saliency Maps and Adversarial Robustness Puneet Mangla Vedant Singh V. Balasubramanian AAML 26 17 0 14 Jun 2020
Evaluations and Methods for Explanation through Robustness Analysis Cheng-Yu Hsieh Chih-Kuan Yeh Xuanqing Liu Pradeep Ravikumar Seungyeon Kim Sanjiv Kumar Cho-Jui Hsieh XAI 50 58 0 31 May 2020
Adversarial Attacks and Defense on Texts: A Survey A. Huq Mst. Tasnim Pervin AAML 130 21 0 28 May 2020
Universal Adversarial Perturbations: A Survey Ashutosh Chaubey Nikhil Agrawal Kavya Barnwal K. K. Guliani Pramod Mehta OOD AAML 80 47 0 16 May 2020
Evaluating and Aggregating Feature-based Model Explanations Umang Bhatt Adrian Weller J. M. F. Moura XAI 92 224 0 01 May 2020
IROF: a low resource evaluation metric for explanation methods Laura Rieger Lars Kai Hansen 57 55 0 09 Mar 2020
Overfitting in adversarially robust deep learning Leslie Rice Eric Wong Zico Kolter 108 804 0 26 Feb 2020
DANCE: Enhancing saliency maps using decoys Y. Lu Wenbo Guo Masashi Sugiyama William Stafford Noble AAML 52 14 0 03 Feb 2020
"How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations Himabindu Lakkaraju Osbert Bastani 65 255 0 15 Nov 2019
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods Dylan Slack Sophie Hilgard Emily Jia Sameer Singh Himabindu Lakkaraju FAtt AAML MLAU 77 819 0 06 Nov 2019
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI Erico Tjoa Cuntai Guan XAI 106 1,447 0 17 Jul 2019
Improving performance of deep learning models with axiomatic attribution priors and expected gradients G. Erion Joseph D. Janizek Pascal Sturmfels Scott M. Lundberg Su-In Lee OOD BDL FAtt 61 81 0 25 Jun 2019
Explanations can be manipulated and geometry is to blame Ann-Kathrin Dombrowski Maximilian Alber Christopher J. Anders M. Ackermann K. Müller Pan Kessel AAML FAtt 81 332 0 19 Jun 2019
Robust Attribution Regularization Jiefeng Chen Xi Wu Vaibhav Rastogi Yingyu Liang S. Jha OOD 50 83 0 23 May 2019
On the Connection Between Adversarial Robustness and Saliency Map Interpretability Christian Etmann Sebastian Lunz Peter Maass Carola-Bibiane Schönlieb AAML FAtt 58 162 0 10 May 2019
Towards a Robust Deep Neural Network in Texts: A Survey Wenqi Wang Benxiao Tang Run Wang Lina Wang Aoshuang Ye AAML 50 39 0 12 Feb 2019