Deepfake Detection Methods and Trends: A Comprehensive Analysis

The accelerated progression of generative artificial intelligence (AI) has facilitated the production of hyper-realistic deepfake media, thereby engendering substantial threats to privacy, security, and societal trust. As techniques for the generation of deepfakes become progressively more advanced, the establishment of effective detection mechanisms has emerged as an essential focus of scholarly enquiry. This paper provides a detailed look at the methods and current trends for detecting AI-generated deepfakes, bringing together the progress made in this area so far. We carefully sort detection methods into two groups: traditional digital forensics (like checking for artefacts and facial inconsistencies) and modern AI-based methods (such as deep learning and anomaly detection). We explain their benefits, limitations, and effectiveness using well-known test datasets. The survey also looks into new problems like attacks that try to trick systems, the ability to apply findings across different areas, and the growing rivalry between technologies that create content and those that detect it. We also look closely at current trends like using different types of data together, attention mechanisms, and self-supervised learning to improve strength against challenges. By closely looking at the newest datasets, ways to evaluate them, and how they are used in real life, this study finds problems with current methods and recommends future research areas, like the need for clear AI, common standards, and adaptable systems to tackle new challenges. This survey functions as a foundational reference for researchers and practitioners seeking to navigate the intricate terrain of deepfake detection and contribute to the establishment of reliable digital media ecosystems.