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arXiv · 2606.22699

Catching Lies Without Sending the Video: Privacy-Preserving Multimodal Deception Detection

Nikita Sharma·Pranav Sara·Karan Singla
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Abstract

Frontier multimodal models can guess whether a person is lying from a testimony video. To do so, they stream that raw face and voice to a third-party model. We ask whether the heavy media is needed at all. On the Real-life Trial Deception dataset, Whissle on-device speech and vision stack extracts a compact digest: transcript, emotion, age, gender, intent distributions, a deception intent filter, fluency and rhythm, per-frame facial behaviour, and prosody. Under speaker-independent evaluation, we report three findings. A small classifier on this digest reaches AUC 0.741, matching Gemini 2.5 Pro on full video. Handing the digest to a frontier LLM reaches AUC 0.755 with Claude Opus 4.8 at 7.8X fewer input tokens, with no media leaving the device. The reported 75% accuracy is a speaker-leakage artifact. We release code and experiments.

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Nikita Sharma, Pranav Sara, Karan Singla. 2026-06-21. Catching Lies Without Sending the Video: Privacy-Preserving Multimodal Deception Detection. https://arxiv.org/abs/2606.22699

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