Detecting the Undetectable: A Review of Cutting-Edge Deep-fake Detection Techniques

Resumo

Nowadays, deep-fake technology has been seen as a powerful tool for producing very realistic but artificial media, thereby threatening privacy, digital security, and the authenticity of visual and audio material. The growing sophistication of Transformer-based models, Autoencoders, and Generative Adversarial Networks (GANs) has made it ever more challenging to distinguish genuine from edited material. Including Convolutional Neural Networks (CNNs), Bayesian Interface models, few shot Learning, and Self-Supervised Transformer Architectures, this review covers modern deep-fake detection methods in great detail. Highlighting their strengths, constraints, and practical utility, we examine the most recent techniques, benchmark data sets, and generalization challenges in deep-fake detection. Furthermore, we assess the effect of cross-dataset performance, adversarial resilience, and real-time detection on internet-of-things and edge devices. We consider the changing field of deep-fake research and suggest possible future courses to improve detection accuracy and resistance against adversarial assaults by comparing different detection methods. The results highlight the pressing need of AI-driven deep-fake detection systems that are adaptive, multimodal, and explainable to address the increasing abuse of synthetic media.