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Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection
April 30, 2024, 4:47 a.m. | Konstantinos Tsigos, Evlampios Apostolidis, Spyridon Baxevanakis, Symeon Papadopoulos, Vasileios Mezaris
cs.CV updates on arXiv.org arxiv.org
Abstract: In this paper we propose a new framework for evaluating the performance of explanation methods on the decisions of a deepfake detector. This framework assesses the ability of an explanation method to spot the regions of a fake image with the biggest influence on the decision of the deepfake detector, by examining the extent to which these regions can be modified through a set of adversarial attacks, in order to flip the detector's prediction or …
abstract arxiv cs.ai cs.cv decisions deepfake detection evaluation explainable ai fake framework image influence paper performance quantitative spot type
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