Journal of Forensic Research

Digital Forensic Investigation of Video Evidence Using Voice Activity Detection Algorithm

Abstract

Ashutosh Deo Tiwari, Thanuja Durgam*, Sunil Kumar and Sunand Bishnoi

Objective: This study investigates the forensic potential of active speaker switching behaviors in video conferencing platforms as a means of establishing platform identification and authenticity when conventional metadata is absent. The work aims to provide a reproducible, system level methodology for software attribution, thereby strengthening the reliability of multimedia evidence in digital forensics.

Methods: An investigative case involving an 18-minute three-person video recording with no overt metadata was analyzed. The recording displayed dynamic screen transitions aligned with speaker activity. To uncover latent forensic markers, a controlled replication experiment was conducted in which the same conversational scenario was reproduced across multiple conferencing platforms (Zoom, Microsoft Teams, Cisco Webex, Zoho Meeting) under identical acoustic and visual conditions. Screen recordings were captured, and frame-by-frame forensic analysis was performed. Parameters such as onset-to-transition latency, debounce thresholds, lip-synchronization alignment, and scene-layout constraints were systematically measured.

Results: The analysis revealed that conferencing platforms embed distinct and reproducible behavioral fingerprints in their Voice Activity Detection (VAD) and Active Speaker Recognition (ASR) pipelines. These included measurable latency windows, stability thresholds, layout aware switching policies, and host-specific exception handling. Platform-specific audiovisual signatures were consistently identified across replications, demonstrating their viability as forensic identifiers.

Conclusion: This digital forensic research establishes active speaker switching dynamics as a robust forensic marker for platform attribution in multimedia evidence analysis. By relying on intrinsic audiovisual behaviors rather than conventional metadata, the method enhances provenance validation, improves evidentiary admissibility, and contributes to digital justice processes. Beyond attribution, the findings have implications for forensic validation of ML-mediated communication systems and the scientific rigor of multimedia forensics.

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