How to Detect Deepfakes in Virtual Meetings

Deepfakes in virtual meetings are becoming more difficult to identify as AI systems produce increasingly synchronized, low-latency audio and video, while conferencing-platform compression and normal conversation complexity can conceal remaining flaws. The article notes that Resemble AI’s Q2 2025 report recorded 487 verified deepfake incidents and $347.2 million in losses over three months, underscoring risks to sensitive activities such as financial approvals, hiring, and executive communications. Potential indicators include irregular facial movements, imperfect lip synchronization, unusual response delays, behavioral deviations, and environmental inconsistencies, although none alone proves manipulation because technical issues can create similar effects. It recommends a layered approach combining secondary-channel identity confirmation, participant-list checks, context-specific and unscripted questions, employee training, risk-based meeting controls, multi-step approval processes, and AI monitoring tools whose alerts prompt review rather than serve as final judgments. Resemble AI presents its platform as a multimodal detection and verification system that can monitor major conferencing services, analyze synthetic-media signals with low latency, compare voice signatures, and provide explainable forensic outputs, while emphasizing that real-world validation and human oversight remain necessary.

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