Review on Deep Learning-Based Deepfake Detection and Multimedia Forensics

The rapid advancement of generative artificial intelligence has significantly accelerated the creation of deepfake multimedia content, including manipulated images, videos, and synthetic speech. Although deepfake technologies offer beneficial applications in entertainment, education, and virtual communication, their misuse poses serious threats to cybersecurity, digital trust, social stability, and multimedia integrity. Consequently, deep learning-based deepfake detection and multimedia forensic techniques have become critical research domains. This review presents a concise overview of recent advancements in deepfake generation, detection methodologies, benchmark datasets, and multimedia forensic frameworks. The paper categorizes major deepfake detection approaches into spatial, temporal, frequency-domain, transformer-based, multimodal, and forensic-oriented techniques. Furthermore, widely used datasets and evaluation metrics are summarized to provide comparative insights into existing systems. Key challenges such as cross-dataset generalization, adversarial robustness, explainability, and real-time deployment are also discussed. Finally, emerging research directions including multimodal forensic intelligence, explainable artificial intelligence, watermarking, and generalized detection frameworks are highlighted. This review aims to provide researchers with a compact yet comprehensive understanding of current developments and future opportunities in deepfake detection and multimedia forensics.