Deepfake Technology and Image Forensics: Advancements, Challenges, and Ethical Implications in Synthetic Media Detection - Jain, Nilakshi; Borade, Shwetambari; Patel, Bhavesh; Kumar, Vineet; Godhrawala, Mustansir; Kolaskar, Shubham; Nagare, Yash; Shah, Pratham; Shah, Jayan (International Journal of Intelligent Systems and Applications in Engineering, 2024)
This comparative analysis delves into the dynamic landscape of deepfake technology and its intricate relationship with image forensics. Focused on advanced machine learning methodologies such as autoencoders, GANs, and CNNs, the exploration reveals both unprecedented possibilities and formidable challenges. While technical advancements showcase innovative solutions with notable accuracies, ethical concerns surrounding potential misuse highlight the urgency for robust detection methods. The versatility of approaches extends beyond detection to applications like image manipulation detection. Evaluation methods, combining subjective assessments and objective evaluations, stress the importance of a holistic understanding of deepfake challenges. This analysis offers a comprehensive snapshot of deepfake detection, showcasing significant strides in countering synthetic media threats. Sustained collaboration, innovation, and interdisciplinary approaches are deemed crucial for staying ahead in the ongoing battle against deepfake misuse.