DeepExplain: Enhancing DeepFake Detection Through Transparent and Explainable AI model

Abstract

The rapid advancement of digital media technologies has given rise to DeepFake videos, synthetic content generated using deep learning algorithms that can convincingly mimic real individuals' appearances and actions. This presents a significant challenge in maintaining digital content integrity, as DeepFakes pose threats to information veracity, security, and public trust. Addressing this challenge necessitates robust detection methods that not only accurately identify DeepFakes but also ensure transparency and understandability in their operations. This study introduces DeepExplain, a new approach that combine convolutional neural networks (CNNs) and long shortterm memory (LSTM) networks, augmented with explainability features to enhance the detection of DeepFakes. Utilizing the comprehensive DeepFake Detection Challenge (DFDC) dataset, DeepExplain demonstrates superior accuracy and balanced performance across essential metrics such as recall, precision, and area under the receiver
operating characteristic (AUROC) curve. Crucially, by integrating explainability mechanisms like Gradientweighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP) values, DeepExplain not only identifies DeepFakes but also provides insights into the decision-making process, fostering trust and facilitating broader understanding. The findings underscore the potential of explainable and transparent AI solutions in combating the evolving threat of DeepFakes, highlighting important directions for future research and
practical applications in digital media verification and security