DeepDect: an explainable AI platform for face swapping and face generation DeepFake detection

The rapid advancement of Deep Learning (DL) techniques has led to the widespread proliferation of DeepFake (DF) images, raising real-world concerns. In this work, DeepDect is proposed and evaluated in a real-world scenario. The platform has been developed using a human-centered approach, integrating insights (requirements) from both common and DF expert users. The platform can detect both face-swapping and generated deepfake faces, which are the most common cases of DF in the current context. Two benchmarks have been conducted to evaluate state-of-the-art DF detection models for face-swap and AI-generated images. The best-performing models (ResNet-50 and Random Forest for face-swapping detection, Capsule Forensics v2, and CNN for AI-generated images) have been integrated into the platform as the detection engine. An Explainable AI (XAI) module has been implemented and integrated into DeepDect to provide visual (Grad-CAM heatmaps) and textual explanations, enhancing interpretability and user trust. A real-world evaluation involving 108 participants was performed to assess DeepDect’s effectiveness compared to human detection. DeepDect has achieved 81% detection accuracy, outperforming human users, underscoring the need for such a tool in real-world applications. These findings have highlighted the importance of accessible, explainable, and high-performing AI solutions, offering a balance between technical robustness and User-Centric Design (UCD).