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Explainable Artificial Intelligence (XAI) aims to uncover the decision-making processes of AI models.
Notes on the N-Person Game - I: Characteristic-Point Solutions of the Four-Person Game
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Learning Deep Features for Discriminative Localization. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016 . IEEE Computer Society, USA, 2921–2929
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Practical Black-Box Attacks against Machine Learning. In Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security (Abu Dhabi, United Arab Emirates) (ASIA CCS ’17) . Association for Computing Machinery, New York, NY, USA, 506–519
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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. In IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017 . IEEE Computer Society, USA, 618–626
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How to steal a machine learning classifier with deep learning. In 2017 IEEE International Symposium on Technologies for Homeland Security (HST) . IEEE, USA, 1–5
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Membership Inference Attacks Against Machine Learning Models. In 2017 IEEE Symposium on Security and Privacy, SP 2017, San Jose, CA, USA, May 22-26, 2017 . IEEE Computer Society, USA, 3–18
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Axiomatic Attribution for Deep Networks. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017 (Proceedings of Machine Learning Research, Vol. 70) , Doina Precup and Yee Whye Teh (Eds.). PMLR, USA, 3319–3328
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Visualizing Deep Neural Network Decisions: Prediction Difference Analysis. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings . OpenReview.net, USA
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Explainable AI Methods - A Brief Overview. In xxAI - Beyond Explainable AI - International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers (Lecture Notes in Computer Science, Vol. 13200) , Andreas Holzinger, Randy Goebel, Ruth Fong, Taesup Moon, Klaus-Robert Müller, and Wojciech Samek (Eds.). Springer, USA, 13–38
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Crop Recommendation Dataset
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High Accuracy and High Fidelity Extraction of Neural Networks. In 29th USENIX Security Symposium, USENIX Security 2020, August 12-14, 2020 , Srdjan Capkun and Franziska Roesner (Eds.). USENIX Association, USA, 1345–1362
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Defending Against Model Stealing Attacks With Adaptive Misinformation. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020 . Computer Vision Foundation / IEEE, USA, 767–775
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Explainable artificial intelligence: A survey. In 41st International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2018, Opatija, Croatia, May 21-25, 2018 , Karolj Skala, Marko Koricic, Tihana Galinac Grbac, Marina Cicin-Sain, Vlado Sruk, Slobodan Ribaric, Stjepan Gros, Boris Vrdoljak, Mladen Mauher, Edvard Tijan, Predrag Pale, and Matej Janjic (Eds.). IEEE, USA, 210–215
Filip Karlo Dosilovic, Mario Brcic, and Nikica Hlupic. 2018 · 2018
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Stealing Neural Networks via Timing Side Channels
Vasisht Duddu, Debasis Samanta, D. Vijay Rao, and Valentina Emilia Balas. 2018 · 2018
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Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (Toronto, Canada) (CCS ’18) . Association for Computing Machinery, New York, NY, USA, 619–633
Karan Ganju, Qi Wang, Wei Yang, Carl A. Gunter, and Nikita Borisov. 2018 · 2018
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Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV). In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 80) , Jennifer Dy and Andreas Krause (Eds.). PMLR, USA, 2668–2677
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Learning how to explain neural networks: PatternNet and PatternAttribution. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings . OpenReview.net, USA
Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, and Sven Dähne. 2018 · 2018
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Anchors: High-Precision Model-Agnostic Explanations. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018 , Sheila A. McIlraith and Kilian Q. Weinberger (Eds.). AAAI Press, USA, 1527–1535
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018 · 2018
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Stealing Hyperparameters in Machine Learning. In 2018 IEEE Symposium on Security and Privacy, SP 2018, Proceedings, 21-23 May 2018, San Francisco, California, USA . IEEE Computer Society, USA, 36–52
Binghui Wang and Neil Zhenqiang Gong. 2018 · 2018
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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, Alberto Barbado, Salvador Garcia, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, and Francisco Herrera. 2020 · 2019
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The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks. In 28th USENIX Security Symposium, USENIX Security 2019, Santa Clara, CA, USA, August 14-16, 2019 , Nadia Heninger and Patrick Traynor (Eds.). USENIX Association, Santa Clara, CA, USA, 267–284
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Sanjay Kariyappa and Moinuddin K. Qureshi. 2020 · 2020
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Overlearning Reveals Sensitive Attributes. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net, USA
Congzheng Song and Vitaly Shmatikov. 2020 · 2020
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XGNN: Towards Model-Level Explanations of Graph Neural Networks. In KDD ’20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23-27, 2020 , Rajesh Gupta, Yan Liu, Jiliang Tang, and B. Aditya Prakash (Eds.). ACM, USA, 430–438
Hao Yuan, Jiliang Tang, Xia Hu, and Shuiwang Ji. 2020 · 2020
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The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020 . Computer Vision Foundation / IEEE, USA, 250–258
Yuheng Zhang, Ruoxi Jia, Hengzhi Pei, Wenxiao Wang, Bo Li, and Dawn Song. 2020 · 2020
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Membership Leakage in Label-Only Exposures. In Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security (Virtual Event, Republic of Korea) (CCS ’21) . Association for Computing Machinery, New York, NY, USA, 880–895
Zheng Li and Yang Zhang. 2021 · 2021
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Deep Neural Network Fingerprinting by Conferrable Adversarial Examples. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021 . OpenReview.net, USA
Nils Lukas, Yuxuan Zhang, and Florian Kerschbaum. 2021 · 2021
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MEGEX: Data-Free Model Extraction Attack against Gradient-Based Explainable AI
Takayuki Miura, Satoshi Hasegawa, and Toshiki Shibahara. 2021 · 2021
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On the Privacy Risks of Model Explanations. In AIES ’21: AAAI/ACM Conference on AI, Ethics, and Society, Virtual Event, USA, May 19-21, 2021 , Marion Fourcade, Benjamin Kuipers, Seth Lazar, and Deirdre K. Mulligan (Eds.). ACM, USA, 231–241
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Jean-Baptiste Truong, Pratyush Maini, Robert J. Walls, and Nicolas Papernot. 2021 · 2021
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Exploiting Explanations for Model Inversion Attacks. In 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021 . IEEE, USA, 662–672
Xuejun Zhao, Wencan Zhang, Xiaokui Xiao, and Brian Y. Lim. 2021 · 2021
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DualCF: Efficient Model Extraction Attack from Counterfactual Explanations. In FAccT ’22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21 - 24, 2022 . ACM, USA, 1318–1329
Yongjie Wang, Hangwei Qian, and Chunyan Miao. 2022 · 2022
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UCI Machine Learning Repository
Markelle Kelly, Rachel Longjohn, and Kolby Nottingham. 2023 · 2023
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Explanation leaks: Explanation-guided model extraction attacks
Anli Yan, Teng Huang, Lishan Ke, Xiaozhang Liu, Qi Chen, and Changyu Dong. 2023 · 2023
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Model interpretability - Azure Machine Learning
Microsoft Azure. 2024 · 2024
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Vertex Explainable AI
Google Cloud. 2021 · 2024
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Watson Openscale
IBM Cloud. 2023 · 2024
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A Survey of Privacy Attacks in Machine Learning
Maria Rigaki and Sebastian García. 2024 · 2024
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Amazon SageMaker Clarify
Amazon Web Services. 2023 · 2024
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