Video call deepfake

Learn what a video call deepfake is, how it works, the risks it poses, and how to detect and mitigate live AI impersonation during video meetings.

What is a video call deepfake?

A video call deepfake is a real-time manipulation of someone’s likeness using AI to impersonate them during live video meetings. Unlike pre-recorded deepfake videos that are edited before distribution, video call deepfakes happen interactively, with artificial intelligence generating or modifying a participant’s face, voice, or gestures on the fly. This emerging fraud tactic can make it appear that a trusted colleague, executive, or candidate is present in a meeting, even when that person has no idea the call is taking place.

As AI models become faster and more accessible, live deepfake video calls are shifting from a futuristic threat to a present-day reality. Fraudsters are already leveraging these technologies to execute scams ranging from business lookalikes to high-stakes corporate fraud. Because these attacks merge visual impersonation with cloned voices and behavioral mimicry, spotting a deepfake in the moment can be extremely difficult.

How does a video call deepfake typically work?

Video call deepfakes are generally powered by generative adversarial networks (GANs) and other machine learning techniques designed to create lifelike synthetic media.

AI-driven facial replacement

Attackers typically use training data like photos, recordings, or even scraped social media clips to build a model of a person’s face. During a live call, the model overlays this synthetic likeness in real time, replicating movements like blinking, lip-syncing, and head turns.

Synthetic voice impersonation

In addition to visuals, fraudsters often pair facial deepfakes with deepfake voices. With only a few minutes of speech samples, modern voice synthesis tools can generate realistic speech patterns that match tone, accent, and cadence. This creates a multi-layered impersonation that strengthens the illusion.

Hybrid attack tactics

Many live deepfake scams use a hybrid strategy, combining audio deepfakes, spoofed phone numbers, and fabricated documents. This blending of attack vectors makes fraud harder to detect with traditional safeguards such as caller ID or static authentication checks.

Why are video call deepfakes a growing threat?

Lower barriers to entry

Only a few years ago, generating a deepfake required technical expertise and powerful GPUs. Now, point-and-click applications make it possible for non-experts to deploy live deepfake video calls with minimal setup.

High-value fraud opportunities

Executives, financial officers, and HR leaders are attractive targets. Fraudsters have used video call deepfakes to request wire transfers, authorize payments, or impersonate job candidates in interviews. Losses from synthetic identity fraud and AI-powered scams can happen across industries.

Erosion of trust in virtual communication

Remote work and digital collaboration rely heavily on video conferencing platforms. As deepfakes spread, the credibility of what people see and hear on screen is being challenged, creating new risks for organizations trying to maintain secure video conferencing.

How to detect a video call deepfake

Even as the technology improves, subtle red flags can reveal a video call deepfake. Awareness is the first step toward detection.

Visual inconsistencies

Behavioral testing

Human vs AI detection

While individuals can sometimes catch flaws through intuition, professional deepfake detection tools analyze micro-signals invisible to the human eye or ear. These include frame-by-frame analysis, audio waveform mismatches, and device-level metadata.

How can organizations better protect against video call deepfakes?

Authentication and access controls

Platforms can integrate multifactor authentication and behavioral analysis to help ensure participants are who they claim to be before joining meetings.

AI-driven detection tools

Solutions such as real-time deepfake detection software analyze video and audio streams for inconsistencies. Coupling these with voice authentication provides stronger defense against impersonation attacks.

Policy, training, and response plans

Organizations should train employees to question unusual requests made over video calls, especially those involving money, credentials, or sensitive data. Clear incident response playbooks help minimize damage if a deepfake scam occurs.