Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection

29 April 2024

Papers citing "Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection"

6 / 6 papers shown

Title
Extending Information Bottleneck Attribution to Video Sequences Veronika Solopova Lucas Schmidt Dorothea Kolossa 47 0 0 28 Jan 2025
Rethinking Attention-Model Explainability through Faithfulness Violation Test Y. Liu Haoliang Li Yangyang Guo Chen Kong Jing Li Shiqi Wang FAtt 116 42 0 28 Jan 2022
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis Thomas Fel Rémi Cadène Mathieu Chalvidal Matthieu Cord David Vigouroux Thomas Serre MLAU FAtt AAML 112 57 0 07 Nov 2021
WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection Bojia Zi Minghao Chang Jingjing Chen Xingjun Ma Yu-Gang Jiang CVBM 80 370 0 05 Jan 2021
Image-to-Image Translation with Conditional Adversarial Networks Phillip Isola Jun-Yan Zhu Tinghui Zhou Alexei A. Efros SSeg 212 19,387 0 21 Nov 2016
Xception: Deep Learning with Depthwise Separable Convolutions François Chollet MDE BDL PINN 201 14,304 0 07 Oct 2016