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Improving the Perturbation-Based Explanation of Deepfake Detectors Through the Use of Adversarially-Generated Samples
Konstantinos Tsigos, Evlampios Apostolidis, Vasileios Mezaris
In this paper, we introduce the idea of using adversarially-generated samples of the input images that were classified as deepfakes by a detector, to form perturbation masks for in…
An Integrated Framework for Multi-Granular Explanation of Video Summarization
Konstantinos Tsigos, Evlampios Apostolidis, Vasileios Mezaris
In this paper, we propose an integrated framework for multi-granular explanation of video summarization. This framework integrates methods for producing explanations both at the fr…
Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection
Konstantinos Tsigos, Evlampios Apostolidis, Spyridon Baxevanakis +2
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 ex…
MMFusion: Combining Image Forensic Filters for Visual Manipulation Detection and Localization
Kostas Triaridis, Konstantinos Tsigos, Vasileios Mezaris
Recent image manipulation localization and detection techniques typically leverage forensic artifacts and traces that are produced by a noise-sensitive filter, such as SRM or Bayar…