Explainable deepfake detection across different modalities: An overview of methods and challenges

The increasing use of deepfake technology enables the creation of realistic and deceptive content, raising concerns about several serious issues, including biometric authentication, misinformation, politics, privacy, and trust. Many Deepfake Detection (DD) models are entering the market to combat the misuse of deepfakes. With these developments, one primary issue occurs in ensuring the explainability of the proposed detection models to understand the rationale of the decision. This paper aims to investigate the state-of-the-art explainable DD models across multiple modalities, including image, video, audio, and text. Unlike existing surveys that focus on detection methodologies with minimal attention to explainability and limited modality coverage, this paper directly focuses on these gaps. It offers a comprehensive analysis of advanced explainability techniques, including Grad-CAM, LIME, SHAP, LRP, Saliency Maps, and Anchors, for detecting deceptive content across the modalities. It identifies the strengths and limitations of existing models and outlines research directions to enhance explainability and interpretability in future works. By exploring these models, we aim to enhance transparency, provide deeper insights into model decisions, and bridge the gap between detection accuracy with explainability in DD models.