Unveiling Deepfakes with Latent Diffusion Counterfactual Explanations

Abstract

Deepfake technology, driven by deep learning, produces highly convincing synthetic media, raising concerns about misuse. While DeepFake detection models have achieved impressive accuracy, but due to the difficulty of distinguishing fake from real, interpretability remains challenging that humans cannot understand or trust the detection results. We propose a novel approach to enhance interpretability by generating counterfactual explanations. By integrating ensemble classifier loss and text instructions into the fine-tuning of a Latent Diffusion Model, our method effectively improves the quality and efficiency of generated counterfactual explanations. Experiments on DeepFake datasets validate the effectiveness of our approach, contributing the interpretability of Deepfake detection.