The Deepfake Litmus Test: A Multimedia Authenticity Mechanism

Deepfake technologies have made it increasingly difficult to distinguish authentic video content from manipulated media. This paper presents a forensic detection framework, referred to as the Litmus Test, which focuses on structural analysis of MP4 container files to detect signs of tampering. Unlike conventional AI-based approaches that operate as black boxes, this method examines the atomic composition of video containers to identify anomalies. The proposed method performs atom-level inspection of MP4 file hierarchies and structural markers to uncover anomalies indicative of synthetic manipulation. Evaluations using datasets such as CelebDF, UADFV, and DeeperForensics reveal that the framework can identify inconsistencies common in deepfake media. The system offers explainable outputs suitable for forensic and legal applications, enabling verifiable detection grounded in digital forensics. By prioritizing interpretability, the framework supports forensic investigations by providing verifiable evidence that can meet the standards required for legal credibility.