Deep-fake Detection of Digital Misinformation Over social media: a comprehensive review

Abstract

The proliferation of deepfake content across social media platforms has intensified the spread of digital misinformation, posing serious threats to public trust, democratic stability, and individual privacy. Existing deepfake detection methods, while technically advanced, face critical limitations in generalizability, real-time performance, and resilience against increasingly sophisticated generative techniques. This review highlights the urgent need for robust and adaptive detection frameworks capable of countering the evolving tactics used to fabricate and disseminate synthetic media.

We systematically examine current approaches, including machine learning algorithms, computer vision techniques, and adversarial training strategies, and evaluate their effectiveness based on dataset quality, modality coverage, and deployment feasibility. Our findings reveal that hybrid models integrating multiple detection modalities consistently outperform single-method systems in accuracy and robustness. The study underscores the importance of aligning technical innovation with social awareness, advocating for scalable solutions that can mitigate misinformation risks amplified by the viral nature of social media platforms.