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"

8 / 8 papers shown

Title
Evaluating Explanation Quality in X-IDS Using Feature Alignment Metrics Mohammed Alquliti Erisa Karafili BooJoong Kang XAI 19 0 0 12 May 2025
Inpainting the Gaps: A Novel Framework for Evaluating Explanation Methods in Vision Transformers Lokesh Badisa Sumohana S. Channappayya 40 0 0 17 Jun 2024
Understanding User Preferences in Explainable Artificial Intelligence: A Survey and a Mapping Function Proposal M. Hashemi Ali Darejeh Francisco Cruz 27 3 0 07 Feb 2023
SoK: Explainable Machine Learning for Computer Security Applications A. Nadeem D. Vos Clinton Cao Luca Pajola Simon Dieck Robert Baumgartner S. Verwer 17 40 0 22 Aug 2022
Auditing Visualizations: Transparency Methods Struggle to Detect Anomalous Behavior Jean-Stanislas Denain Jacob Steinhardt AAML 10 7 0 27 Jun 2022
TRAPDOOR: Repurposing backdoors to detect dataset bias in machine learning-based genomic analysis Esha Sarkar Michail Maniatakos 24 3 0 14 Aug 2021
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization Judy Borowski Roland S. Zimmermann Judith Schepers Robert Geirhos Thomas S. A. Wallis Matthias Bethge Wieland Brendel FAtt 21 7 0 23 Oct 2020
SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems Edward Chou Florian Tramèr Giancarlo Pellegrino AAML 168 286 0 02 Dec 2018