Authors: Chirayu C.Jadhav, Omkar K. Pol, Rushikesh M. Amane, Associate Professor Mrs. M. M. Raste
Abstract: The rapid advancement of deep learning has enabled the creation of hyper-realistic synthetic media, commonly known as deepfakes, which threaten digital trust, privacy, and security. While these technologies demonstrate the potential of generative models like GANs, their misuse for misinformation and identity fraud necessitates robust detection methods. This paper presents a comprehensive analysis of state-of-the-art deepfake generation techniques and their countermeasures, focusing on the challenges of distinguishing manipulated content from authentic media. We evaluate data-driven detection approaches, including artifact-based analysis and deep neural networks, highlighting their strengths and limitations under varying compression levels and dataset scales. Building on existing benchmarks like FaceForensics++ and Celeb-DF, we propose a systematic framework for assessing detector performance, emphasizing the role of domain-specific features (e.g., facial micro-expressions, inconsistent lighting) in improving accuracy. Our experiments demonstrate that hybrid methods—combining spatial-temporal analysis with adversarial training—outperform human observers and single-modality detectors, particularly in cross-dataset scenarios. Finally, we discuss emerging threats, such as adaptive deepfakes designed to evade detection, and outline future directions for scalable, real-time solutions. This work aims to standardize evaluation metrics and inspire novel research to safeguard digital media integrity in an era of escalating synthetic threats.
