An Advanced Deep Learning Approach for Detecting Deepfake Video Manipulation

Deepfake technology poses several challenges from the legal, ethics, and sociological aspects since it utilizes deep-learning models that are relatively very advanced for creating lifelike synthetic media. Detecting fake synthetic content goes a long way in ensuring trust and integrity in the use of digital media. This paper presents an improved deepfake detection method that utilizes a Variational Autoencoder (VAE) for feature extraction, Vision Transformers (ViTs) to capture spatial and temporal dependencies, and a generator-discriminator pair inspired by Generative Adversarial Networks (GANs) for classification and Multi-task Cascaded Convolutional Networks (MTCNN) for efficient face detection and alignment. The proposed framework capitalizes on the strengths of these models to identify temporal and spatial anomalies often present in manipulated video content. The system integrates all these techniques and offers a holistic and robust approach to deepfake detection, thereby addressing challenges like occlusions, diverse lighting conditions, and sophisticated manipulation strategies. Experimental evaluations on benchmark datasets have demonstrated the high accuracy and low false-positive rates of the framework, even in the most sophisticated deepfake generation techniques. It has contributed to the growing body of work in deepfake detection as it presents a modular, scalable, and fully automated approach towards adapting to further advancements in the generation of synthetic media.