"Robust Deepfake Detection using Learning and Forensic Features"

Abstract: Deepfake technology has rapidly evolved, enabling the creation of highly realistic synthetic media that can deceive both humans and machines. This research aims to develop a robust deepfake detection framework by integrating deep learning techniques with forensic feature analysis. The proposed system extracts spatial, temporal, and physiological inconsistencies from video and image data to identify synthetic manipulations effectively. Advanced neural architectures such as convolutional neural networks (CNNs) and transformer-based models are combined with handcrafted forensic cues like frequency domain artifacts and texture irregularities. Experimental evaluation on benchmark datasets demonstrates improved accuracy and resilience against adversarial deepfakes. The results indicate that hybrid learning–forensic models offer a promising direction for enhancing media authenticity verification.

Keywords: Deepfake Detection, Forensic Features, Deep Learning, Convolutional Neural Networks (CNN), Transformer Models, Media Forensics, Hybrid Framework, Adversarial Robustness, Synthetic Media, Video Authentication