What is a deepfake

How does deepfake technology work?

A deepfake learns the patterns that define how a person looks, sounds, and moves. It then generates new content that reflects those similar patterns convincingly enough to fool you.

The mechanics behind how deepfakes predict patterns run in three stages:

1. Training: The model is fed public data on the target, including call recordings, video call images, and even social media clips. The higher the volume of data fed to the prediction engine, the more believable the output. Executives with a large public footprint are at higher risk because there’s so much raw material to clone them from. Deepfake attackers use existing tools like Xanthorox AI to clean audio from calls, podcasts, or investor presentations, producing a cloneable voice model ready for deployment. They pair it with images assembled from LinkedIn headshots, press photographs, or broadcast appearances, and are often also able to train higher-quality video models from scraped footage.
2. Generation: Using patterns extracted from the input, the model produces synthetic content, anything from cloning a voice from three seconds of audio to grafting a face onto a live video feed. Commercial voice-cloning services can be misused for this, and open-source face-swap tools like DeepFaceLive, SimSwap, and Roop produce video that easily passes casual scrutiny.
3. Real-time synthesis: Here the enterprise risk gets sharper, because the deepfake runs live instead of being recorded and edited in advance. By the time your security team can investigate, the call is over. Synthetic feeds are injected into Zoom, Teams, or Webex through OBS Virtual Camera or the Deepfake Offensive Toolkit, which registers as standard webcam drivers that conferencing clients accept without secondary validation. On mobile, attackers use Android emulators like BlueStacks or NoxPlayer to spoof device fingerprints in attacks on mobile-native apps.

How can you spot a deepfake?

Video technology has evolved tremendously in recent years, incorporating accurate facial micro-expressions and lip sync, as well as the ability to capture voice pitch, cadence, tone and speech patterns, all using artificial intelligence workflows.

Deepfakes are difficult to detect because they tend to exploit a person’s trust in seeing a familiar face or hearing a known voice. This is what makes deepfake technology so dangerous. SOC teams in fintech, HR and IT organizations require specialized, purpose-built tools to detect, analyze and flag deepfake content, without which standard authentication controls fail to differentiate between synthetic and authentic identities.

Diopter’s experts encourage organizations to pay close attention to a certain set of indicators to spot a carefully crafted deepfake:

Video indicators

• 5–15 ms lip-sync drift, most visible on playback
• Face-swaps degrade at sharp angles. Look for static framing that avoids head turns past 45 degrees.
• Face-swaps often degrade mid-session, with the face or behavior becoming less consistent
• Codec markers from non-standard video drivers are embedded in the stream header and may be inconsistent with the device model claimed in the session.

Audio indicators

• Background audio with no ambient noise, no breathing between sentences, and no microphone handling sounds
• A consistent 400–600 ms pause before every answer, whether the question is simple or hard
• Unnatural distortions in the high-frequency audio range that are detectable with standard analysis tools
• One-directional conversation structure that resists genuine back-and-forth

Communication and context indicators

• Meeting invites arriving via WhatsApp, SMS, or direct links outside the corporate calendar
• The caller number doesn’t match the HR directory
• The email display name shows an executive, but the reply-to address belongs to the attacker. Always expand the sender field before responding.
• Lookalike domains that swap one or two characters to mimic a legitimate address
• Video feed timestamps that don’t match the session time

What are some emerging deepfake trends?

Here are developments in deepfake trends that can help CISOs and fraud teams define their threat roadmap:

• Real-time synthetic video calls: Face-swap tools used on live calls make it harder to verify who’s really speaking. Vishing operations grew by 442% between the first and second half of 2024, according to CrowdStrike.
• Multilingual voice cloning: AI voice tools with real-time translation can let attackers impersonate people across different regions and languages with a single voice model, without retraining.
• Deepfake-as-a-Service: Low-skilled criminals can now use online platforms to create fake videos, voices, and identities for as little as $50 a month, and there’s been a 700% rise in daily AI-related scam calls.
• Synthetic identity insider infiltration: Threat actors, including state-sponsored operatives, are getting through full enterprise hiring pipelines using stolen US identities reinforced with AI. Department of Justice filings name 479 corporate victims, and Mandiant reports nearly every Fortune 500 firm has received dozens of such applications from North Korean operatives.
• Autonomous agent-led campaigns: AI agents that run complex tasks on their own can now contact targets, adjust their responses, and keep conversations going without a human involved.

Common deepfake attacks in cybercrime and fraud

Deepfake attacks occur across multiple attack surfaces, each with distinct goals, techniques, and execution sequences.

Let’s look at a few types of attacks faced by enterprises across the world:

1. Executive impersonation

Attackers clone an executive’s voice and video presence to obtain unauthorized financial approvals. In the most documented variant, every participant on a video call except the victim is synthetic, leading to manipulating the victim’s trust through manufactured consensus. Diopter’s executive impersonation solution is built to catch exactly this attack pattern.

Attackers often pair open-source face-swap tools like DeepFaceLive or SimSwap with a cloned voice model, then use virtual camera software such as OBS Virtual Camera to inject the feed into the conferencing client. Most call invites bypass the corporate calendar, and transfers stay below fraud detection thresholds, while synthetic participants most often back each other’s instructions.

2. BEC attacks

Business email compromise attacks add deepfake-powered voice or video confirmation to fraudulent requests, making them appear more legitimate to finance and operations teams.

Most BEC attacks have the following markers:

• Attackers register lookalike domains designed to pass a quick visual scan.
• Always expand the sender field to check whether a legitimate executive’s display name hides a reply-to address that goes somewhere else.
• Voice cloning closes the verification loop when the target calls to confirm, turning the phone-verification reflex against the person who was trained to use it.

3. Account takeover enablement

Real-time face-swapping tools bypass video-based identity verification checks, allowing attackers to open fraudulent accounts or onboard synthetic identities at scale.

Attackers can buy stolen national ID cards cheaply on carding markets, create active liveness with faceswapping tools, use a virtual camera driver to intercept the pipeline between a physical webcam and the KYC application, fooling the platform into believing a synthetic feed is reading a live camera.

4. Help desk vishing

The IT help desk is a critical access point, and a consistently exploited one. Attackers call the IT help desk, impersonating a privileged employee, to obtain a password reset or MFA re-enrollment before pivoting directly into the identity provider’s infrastructure.

The targets for such attacks are pre-profiled using LinkedIn or breach data from DeHashed or IntelX, well enough to pass most verification questions without a cloned voice.

5. Deepfake financial fraud

Most fraud teams assume they will catch the anomaly before funds move. However, the average time between a call and discovery is under 2 hours. Most organizations have no control sitting inside that window.

Here is a process timeline of an average financial fraud incident:

• Reconnaissance: The attacker maps the target’s finance approvers, reporting lines, and wire thresholds through LinkedIn and public filings. Thirty seconds of a CFO’s voice is enough to build a working clone.
• Infrastructure: The attacker registers a lookalike domain, trains a voice model, and routes a face-swap feed through a virtual camera driver that the conferencing client accepts as a legitimate webcam. Bank accounts to receive the funds are set up across several jurisdictions.
• The approach: The target receives a meeting invite via WhatsApp or a direct link, bypassing the corporate calendar entirely. The pretext is usually confidential.
• The call: Every participant except the victim is synthetic. The deepfaked CFO leads, other synthetic executives back up the request, and the wire details are given verbally. The target is told not to discuss the matter before the transfer clears.
• Fund movement: Transfers are split across multiple transactions, each below the automated fraud detection limit.
• Discovery: By the time someone reaches the real executive, who knew nothing about the call, on a separate channel, the transfer chain is already complete.

How to detect deepfakes in company environments

By the time a fake audio or video is flagged by the AI fraud detection systems in an enterprise environment, the damage may already be done. That is why, deepfake defense for enterprise environments has to be treated as a separate layer of security, with its own identity checks and approval workflows.

An effective enterprise deepfake defense system depends on a set of layered checks:

• Media checks: AI detection tools can spot lip-sync issues, voice inconsistencies, and other signs of synthetic media that people miss. They work best on high-risk actions that involve financial approvals, executive calls, or employee onboarding. NIST SP 800-63-4 (2025) requires injection-attack detection as a mandatory control objective for identity verification at higher assurance levels.
• Identity continuity checks: Scoring identity throughout a call catches drift that starts mid-session.
• Behavioral checks: Urgent requests, pressure to act quickly, and inconsistent wording around a high-value transaction are major warning signs.
• Contextual checks: Requests from unfamiliar communication channels or informal platforms like WhatsApp, SMS, or direct links that bypass the corporate calendar; off-hours activity inconsistent with the claimed location; and call latency above 300 milliseconds across sessions are all markers of a deepfake attack.
• Identity checks: Security teams should treat strange login activity or unusual access requests from unknown devices as immediate triggers for verification. Treat MFA reset requests for privileged accounts citing inaccessible recovery methods as an immediate trigger for mandatory callback verification. Pre-agreed verification phrases and out-of-band checks or requirements for secondary approvals work well against the threat of deepfake-enabled fraud.

Best practices to reduce deepfake risk

Organizations need to combine governance, technical safeguards, and employee readiness to reduce deepfake attack risk.

• Governance and verification protocols: Companies need clear approval processes for financial decisions and for access to sensitive information. High-value requests should always go through multiple layers of verification through trusted channels. Security teams also need clear escalation procedures for any incident. Mandatory two-person approval above wire thresholds defeats the multi-participant conference fraud pattern at policy level, with no technology required.
• Human readiness: Employees need practical training on how deepfake attacks work in the real world. An updated security awareness program and regular simulation exercises help employees pause before acting on unusual requests.
• Technical defenses: A strong technical defense layer includes anti-phishing multifactor authentication, identity monitoring across cloud and SaaS environments, and email security tools that only allow trusted devices. For organizations verifying identity over video, KYC vendors must demonstrate NIST SP 800-63-4 compliance.

How Diopter AI closes the gap

Diopter is purpose-built for the attack surface where deepfake fraud lands: live video and voice conversations. The platform runs three simultaneous detection layers:

• Identity verification, with continuous identity drift monitoring;
• Synthetic media detection, for real-time voice and video frame analysis; and
• Conversation arc analysis, which scores the behavioral manipulation sequence regardless of whether synthetic media is present.

All three layers feed into a single verdict: Verified, Potential Threat, Suspected Threat, or High-Risk Threat, and trigger an automated response before a wire or credential hand-off executes. Verdicts are routed directly to your SIEM, ticketing system, or webhook. Detection runs on-device, so raw call media never leaves your perimeter.

If your verification architecture isn’t scoring the arc, it can’t detect the attack. Book a 30-minute live walkthrough with the Diopter team, and we will replay a real deepfake incident against your call environment and show you exactly where your current controls would have failed.