Authentic Vision

Deepfake manipulation has become a significant challenge in digital media. This research focuses on an advanced deepfake detection framework using Inception-ResNetv1 along with Gradient-weighted Class Activation Mapping (Grad-CAM) to enhance interpretability. Our approach ensures high accuracy and explainability, allowing users to visualize decision-making areas in images. The system incorporates Multi-task Cascaded Convolutional Networks (MTCNN) for facial detection and alignment, improving overall performance. A Gradio-based UI enhances usability for technical and non-technical users. This paper outlines the architecture, implementation, and advantages of our method.