Exploring Deep Fake Face Detection: Leveraging Machine Learning with Diverse GAN Models

This study introduces an innovative framework for deepfake facial image detection by integrating machine learning techniques with GAN-based image synthesis. As synthetic media technologies advance, the proliferation of deepfakes has emerged as a critical threat to digital identity, media authenticity, and cybersecurity. To address this challenge, the proposed approach employs a Deep Convolutional Generative Adversarial Network (DCGAN), which serves a dual purpose: generating realistic fake facial images and reusing its discriminator network for real/fake image classification. The model is trained over multiple epochs, allowing both the generator and discriminator to progressively refine their understanding of facial features. Designed without a graphical user interface, the lightweight architecture is optimized for real-time performance and deployment in low-resource environments, such as IoT systems and mobile platforms. The system's effectiveness is validated using standard evaluation metrics including accuracy, precision, recall, and F1-score. Results confirm the model's high detection capability with minimal computational cost. By unifying generation and detection processes within a single framework, this work contributes to the development of efficient adversarial learning-based security solutions.