What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors

22 September 2020

Papers citing "What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors"

38 / 38 papers shown

Title
An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks Ruixiang Tang Mengnan Du Ninghao Liu Fan Yang Xia Hu AAML 57 188 0 15 Jun 2020
Proxy Tasks and Subjective Measures Can Be Misleading in Evaluating Explainable AI Systems Zana Buçinca Phoebe Lin Krzysztof Z. Gajos Elena L. Glassman ELM 73 284 0 22 Jan 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 520 42,449 0 03 Dec 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 121 6,269 0 22 Oct 2019
Hidden Trigger Backdoor Attacks Aniruddha Saha Akshayvarun Subramanya Hamed Pirsiavash 83 624 0 30 Sep 2019
When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures Gil Fidel Ron Bitton A. Shabtai FAtt GAN 39 120 0 08 Sep 2019
Invisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization Shaofeng Li Minhui Xue Benjamin Zi Hao Zhao Haojin Zhu Dali Kaafar 51 60 0 06 Sep 2019
Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems Bao Gia Doan Ehsan Abbasnejad Damith C. Ranasinghe AAML 50 66 0 09 Aug 2019
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning Fan Yang Mengnan Du Xia Hu XAI ELM 57 67 0 16 Jul 2019
Evaluating Explanation Methods for Deep Learning in Security Alexander Warnecke Dan Arp Christian Wressnegger Konrad Rieck XAI AAML FAtt 45 93 0 05 Jun 2019
Fooling Neural Network Interpretations via Adversarial Model Manipulation Juyeon Heo Sunghwan Joo Taesup Moon AAML FAtt 95 204 0 06 Feb 2019
On the (In)fidelity and Sensitivity for Explanations Chih-Kuan Yeh Cheng-Yu Hsieh A. Suggala David I. Inouye Pradeep Ravikumar FAtt 75 453 0 27 Jan 2019
Ablation Studies in Artificial Neural Networks Richard Meyes Melanie Lu Constantin Waubert de Puiseau Tobias Meisen 54 215 0 24 Jan 2019
Interpretable Deep Learning under Fire Xinyang Zhang Ningfei Wang Hua Shen S. Ji Xiapu Luo Ting Wang AAML AI4CE 108 171 0 03 Dec 2018
SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems Edward Chou Florian Tramèr Giancarlo Pellegrino AAML 219 292 0 02 Dec 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 141 1,967 0 08 Oct 2018
Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation C. Liao Haoti Zhong Anna Squicciarini Sencun Zhu David J. Miller SILM 87 313 0 30 Aug 2018
A Benchmark for Interpretability Methods in Deep Neural Networks Sara Hooker D. Erhan Pieter-Jan Kindermans Been Kim FAtt UQCV 111 682 0 28 Jun 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 126 941 0 20 Jun 2018
A Human-Grounded Evaluation Benchmark for Local Explanations of Machine Learning Sina Mohseni Jeremy E. Block Eric D. Ragan FAtt XAI 76 61 0 16 Jan 2018
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning Xinyun Chen Chang-rui Liu Yue Liu Kimberly Lu Basel Alomair AAML SILM 143 1,840 0 15 Dec 2017
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 217 1,842 0 30 Nov 2017
The (Un)reliability of saliency methods Pieter-Jan Kindermans Sara Hooker Julius Adebayo Maximilian Alber Kristof T. Schütt Sven Dähne D. Erhan Been Kim FAtt XAI 101 685 0 02 Nov 2017
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks Aditya Chattopadhyay Anirban Sarkar Prantik Howlader V. Balasubramanian FAtt 112 2,300 0 30 Oct 2017
Interpretation of Neural Networks is Fragile Amirata Ghorbani Abubakar Abid James Zou FAtt AAML 133 867 0 29 Oct 2017
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain Tianyu Gu Brendan Dolan-Gavitt S. Garg SILM 127 1,772 0 22 Aug 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 76 1,520 0 11 Apr 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 321 20,023 0 07 Oct 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,990 0 16 Feb 2016
Learning Deep Features for Discriminative Localization Bolei Zhou A. Khosla Àgata Lapedriza A. Oliva Antonio Torralba SSL SSeg FAtt 250 9,326 0 14 Dec 2015
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,020 0 10 Dec 2015
Evaluating the visualization of what a Deep Neural Network has learned Wojciech Samek Alexander Binder G. Montavon Sebastian Lapuschkin K. Müller XAI 136 1,194 0 21 Sep 2015
You Only Look Once: Unified, Real-Time Object Detection Joseph Redmon S. Divvala Ross B. Girshick Ali Farhadi ObjD 705 36,958 0 08 Jun 2015
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 248 4,672 0 21 Dec 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.7K 100,386 0 04 Sep 2014
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 1.7K 39,547 0 01 Sep 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 312 7,308 0 20 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 595 15,893 0 12 Nov 2013