Interpretable Deep Learning under Fire

3 December 2018

Xinyang Zhang

Ningfei Wang

Hua Shen

Papers citing "Interpretable Deep Learning under Fire"

29 / 29 papers shown

Title
Explainable Graph Neural Networks Under Fire Zhong Li Simon Geisler Yuhang Wang Stephan Günnemann M. Leeuwen AAML 40 0 0 10 Jun 2024
From Attack to Defense: Insights into Deep Learning Security Measures in Black-Box Settings Firuz Juraev Mohammed Abuhamad Eric Chan-Tin George K. Thiruvathukal Tamer Abuhmed AAML 27 0 0 03 May 2024
Stability of Explainable Recommendation Sairamvinay Vijayaraghavan Prasant Mohapatra AAML 38 1 0 03 May 2024
Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape Tiejin Chen Wenwang Huang Linsey Pang Dongsheng Luo Hua Wei OOD 41 0 0 09 Mar 2024
Single-Class Target-Specific Attack against Interpretable Deep Learning Systems Eldor Abdukhamidov Mohammed Abuhamad George K. Thiruvathukal Hyoungshick Kim Tamer Abuhmed AAML 25 2 0 12 Jul 2023
Valid P-Value for Deep Learning-Driven Salient Region Daiki Miwa Vo Nguyen Le Duy I. Takeuchi FAtt AAML 24 14 0 06 Jan 2023
"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice Giovanni Apruzzese Hyrum S. Anderson Savino Dambra D. Freeman Fabio Pierazzi Kevin A. Roundy AAML 31 75 0 29 Dec 2022
Interpretations Cannot Be Trusted: Stealthy and Effective Adversarial Perturbations against Interpretable Deep Learning Eldor Abdukhamidov Mohammed Abuhamad Simon S. Woo Eric Chan-Tin Tamer Abuhmed AAML 25 9 0 29 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 32 4 0 09 Nov 2022
SoK: Explainable Machine Learning for Computer Security Applications A. Nadeem D. Vos Clinton Cao Luca Pajola Simon Dieck Robert Baumgartner S. Verwer 29 40 0 22 Aug 2022
Efficiently Training Low-Curvature Neural Networks Suraj Srinivas Kyle Matoba Himabindu Lakkaraju F. Fleuret AAML 23 15 0 14 Jun 2022
Backdooring Explainable Machine Learning Maximilian Noppel Lukas Peter Christian Wressnegger AAML 14 5 0 20 Apr 2022
Debiased-CAM to mitigate systematic error with faithful visual explanations of machine learning Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 19 1 0 30 Jan 2022
Low-Rank Constraints for Fast Inference in Structured Models Justin T. Chiu Yuntian Deng Alexander M. Rush BDL 29 13 0 08 Jan 2022
TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems Bao Gia Doan Minhui Xue Shiqing Ma Ehsan Abbasnejad D. Ranasinghe AAML 38 53 0 19 Nov 2021
DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications Dongqi Han Zhiliang Wang Wenqi Chen Ying Zhong Su Wang Han Zhang Jiahai Yang Xingang Shi Xia Yin AAML 16 76 0 23 Sep 2021
Jointly Attacking Graph Neural Network and its Explanations Wenqi Fan Wei Jin Xiaorui Liu Han Xu Xianfeng Tang Suhang Wang Qing Li Jiliang Tang Jianping Wang Charu C. Aggarwal AAML 39 28 0 07 Aug 2021
Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks Yulong Cao* Ningfei Wang* Chaowei Xiao Dawei Yang Jin Fang Ruigang Yang Qi Alfred Chen Mingyan D. Liu Bo-wen Li AAML 24 217 0 17 Jun 2021
TrojanZoo: Towards Unified, Holistic, and Practical Evaluation of Neural Backdoors Ren Pang Zheng-Wei Zhang Xiangshan Gao Zhaohan Xi S. Ji Peng Cheng Xiapu Luo Ting Wang AAML 27 31 0 16 Dec 2020
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 18 18 0 10 Dec 2020
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors Yi-Shan Lin Wen-Chuan Lee Z. Berkay Celik XAI 26 93 0 22 Sep 2020
Model extraction from counterfactual explanations Ulrich Aivodji Alexandre Bolot Sébastien Gambs MIACV MLAU 27 51 0 03 Sep 2020
A simple defense against adversarial attacks on heatmap explanations Laura Rieger Lars Kai Hansen FAtt AAML 25 37 0 13 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 19 66 0 26 Jun 2020
Adversarial Infidelity Learning for Model Interpretation Jian Liang Bing Bai Yuren Cao Kun Bai Fei-Yue Wang AAML 44 18 0 09 Jun 2020
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey Jérôme Rony Soufiane Belharbi Jose Dolz Ismail Ben Ayed Luke McCaffrey Eric Granger 25 70 0 08 Sep 2019
Interpreting Adversarial Examples by Activation Promotion and Suppression Kaidi Xu Sijia Liu Gaoyuan Zhang Mengshu Sun Pu Zhao Quanfu Fan Chuang Gan X. Lin AAML FAtt 12 43 0 03 Apr 2019
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 317 11,681 0 09 Mar 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 261 3,109 0 04 Nov 2016