Deepfake Filters in Live Streams: API- and Blockchain-Based Solutions

Live streaming technologies now play a central role in digital communication, creating new opportunities for interaction while simultaneously heightening vulnerabilities to Deepfake manipulation. Real-time synthetic media has accelerated from experimental research to widespread misuse, placing political discourse, financial transactions, and personal security at risk. Traditional detection methods often dependent on pre-recorded material-struggle to keep pace with the rapidity and dynamism of live broadcasts. This review explores how integrated API-based detection systems and Blockchain-enabled verification frameworks can be combined to enhance the reliability of Deepfake detection in live streams. Drawing upon studies published between ۲۰۲۲ and ۲۰۲۵, the review examines algorithmic approaches, architectural considerations, latency constraints, and security properties that together constitute a hybrid defence model. A narrative synthesis evaluates the strengths and weaknesses of each approach, with emphasis on scalability, computational efficiency, privacy, and metadata integrity. The findings suggest that API-driven real-time filters, when fortified with decentralised Blockchain authentication, can bridge the gap between rapid detection and tamper-proof verification. The paper concludes with recommendations for implementation and identifies emerging research directions, particularly in multimodal detection, federated learning, and cross-platform standards for trustworthy live media ecosystems.

کلیدواژه ها:

Deepfake Detection ، Live Streaming Security ، Real-Time Video Analysis ، Blockchain Technology ، API Integration ، Hybrid Systems