Deepfake Detection and Generation Using Machine Learning Techniques

This research paper focuses on Deepfake Detection and Generation using Machine Learning techniques. Deepfakes are AI-generated synthetic media where a person’s face, voice, or actions are realistically manipulated using advanced deep learning models such as Generative Adversarial Networks (GANs) and Diffusion Models. While deepfakes have useful applications in entertainment and content creation, they also pose serious threats such as misinformation, identity fraud, and privacy violations. The study presents a comprehensive analysis of both deepfake generation and detection methods. For detection, the system uses modern approaches including Convolutional Neural Networks (CNNs), Vision Transformers (ViT), and advanced forensic techniques like physiological signal analysis (rPPG) and frequency-domain analysis (DCT). These methods help identify subtle inconsistencies in fake media that are difficult to detect with traditional techniques. A major contribution of this research is highlighting the generalization problem, where detection models perform well on academic datasets but show a significant drop in accuracy on real-world (in-the-wild) data. The paper also discusses robustness challenges such as video compression and adversarial attacks. Finally, the research explores future directions, including multimodal deepfake detection, explainable AI (XAI), and digital content provenance standards like C2PA, aiming to build a more trustworthy and secure digital media ecosystem.