When and How to Fool Explainable Models (and Humans) with Adversarial Examples

5 July 2021

Papers citing "When and How to Fool Explainable Models (and Humans) with Adversarial Examples"

5 / 5 papers shown

Title
Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networks Jon Vadillo Roberto Santana J. A. Lozano Marta Z. Kwiatkowska BDL AAML 58 0 0 17 Feb 2025
Robust deep learning-based semantic organ segmentation in hyperspectral images Silvia Seidlitz Jiansheng Fang Jan Odenthal Berkin Özdemir H. Fu ... Jiang-Dong Liu Martin Wagner Felix Nickel Beat P. Müller-Stich Lena Maier-Hein 21 8 0 09 Nov 2021
Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data F. Cartella Orlando Anunciação Yuki Funabiki D. Yamaguchi Toru Akishita Olivier Elshocht AAML 48 71 0 20 Jan 2021
A Survey on Neural Network Interpretability Yu Zhang Peter Tiño A. Leonardis K. Tang FaML XAI 134 654 0 28 Dec 2020
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 225 3,658 0 28 Feb 2017