A Novel X-Iv2 Ensemble Model for Deepfake Detection with Robustness Analysis and Explainable AI Approach

Abstract

Deepfake technology employs state-of-the-art deep learning to create hyper-realistic synthetic media, which has critical implications for digital authenticity and information security. The spread of such faked content has dire social consequences, facilitating misinformation campaigns, identity theft, and loss of trust in digital media. Today's detection systems experience limitations, such as single-model architectures tending to be unable to generalize the variety of artifact patterns in varying deepfake generation techniques, and most methods are not robust to adversarial attacks. To address these challenges, this study introduces an ensemble-based approach, namely the X-Iv2 Ensemble approach, merging Inception ResNet v2 and Xception Net based on their complementary architectures to enhance feature extraction and classification. The approach utilizes a region-based preprocessing technique, dividing input faces into Upperface, Lowerface, and Eyes regions using facial landmarks on four varied datasets such as DFDC, Celeb-DF, FF++ and SDFVD to ensure robust generalization. Notably, the inclusion of varied races, ages, and genders in the dataset ensures equitable performance across demographics. Apart from these improvements, most deep learning detection frameworks are still black boxes with limited interpretability and practical applications. This approach integrates Explainable AI (XAI) using Integrated Gradients to interpret decision-making processes, while adversarial testing through input perturbations comprehensively assesses the robustness of the model. Extensive assessment metrics such as accuracy (97%), sensitivity (97%), and specificity (96%) illustrate the system's better performance in balancing deepfake detection effectiveness and generalization across datasets.