@sasanikarthikeyasagar48470

This project presents an advanced AI-driven system designed to accurately detect whether a human face in an image or video is real or artificially generated (deepfake). With the rapid growth of deepfake technologies—often created using techniques like Generative Adversarial Networks (GANs)—it has become increasingly difficult to distinguish authentic content from manipulated media. This creates serious risks in areas such as misinformation, identity fraud, cybersecurity, and digital trust. Our solution aims to address this challenge by leveraging the power of deep learning. The system is built using state-of-the-art Deep Learning techniques, particularly Convolutional Neural Networks (CNNs), which are highly effective for image analysis tasks. The model is trained on a diverse dataset containing both real and deepfake facial samples. During training, it learns to identify subtle patterns and anomalies that are often invisible to the human eye. These include inconsistencies in skin texture, unnatural lighting or shadows, irregular eye blinking, facial boundary distortions, and compression artifacts introduced during the deepfake generation process. The workflow of the system begins with input acquisition, where the user can provide an image or a video stream. The system then performs face detection and extraction to isolate the facial region from the input. This is followed by preprocessing steps such as resizing, normalization, and enhancement to ensure optimal model performance. The processed face is then passed through the trained neural network, which analyzes multiple layers of features and produces a classification output—either “Real” or “Fake”—along with a confidence score. One of the key strengths of this project is its ability to perform near real-time detection, making it suitable for practical applications. It can be integrated into social media platforms to flag manipulated content, used in security systems for identity verification, or deployed in digit

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