Study on Deepfake Detection Techniques: Challenges, Limitations and Future Directions

As AI-generated content such as deepfakes continue to evolve, it will become increasingly important to figure out what is real and what is fake. The current deepfake detection techniques, challenges and limitations are studied in this paper. We look at how DL models like CNNs and transformers have been used and their accuracy and effectiveness on various datasets. Nonetheless, the existing literature is wanting in robust generalizability across deepfake types and real-world applications. Our work identifies this gap and suggests areas to improve detection accuracy through hybrid schemes, algorithm advancement, and multi-modal analysis. The growing threat of deepfakes in misinformation, fraud, and security motivates the research. In the end, the paper talks about future policies and the need for establishing benchmark validation frameworks.