Diopter AI: a real-time deepfake detection tool for video and voice calls

Is Diopter AI worth using in 2026?

Yes, for organizations that need to protect high-stakes video and voice calls from deepfakes and AI social engineering.

Diopter AI monitors calls and video for policy alignment, social engineering tactics, and AI impersonation, and verifies wire instructions. Rather than acting as a single-frame deepfake checker, it evaluates each call as a structured conversation event and issues a verdict at each stage of the interaction.

It is best suited to teams where identity verification and high-trust communication are critical, and where a call can move money, access, or a hiring decision.

Summary verdict

Choose Diopter AI if your priority is catching synthetic media and social engineering on live calls before an irreversible action is approved. It is especially effective where wire transfers, executive approvals, remote onboarding, and remote interviews create measurable risk.

This is Cllimber's assessment rather than a claim from Diopter's site: real-time detection across both video and audio is, in our view, the single most important feature in this category, because most deepfake tools check a saved file after the fact, by which point the fraudulent call is already over. On that basis Cllimber rates Diopter AI as a strong fit for banks, fintechs, and enterprise finance teams, precisely because it is designed to act during the call rather than after it.

Why deepfake defense matters now

Deepfake and AI-driven social engineering attacks on businesses have climbed sharply over the last two years. The figures below are drawn from the primary sources (CrowdStrike, Deloitte, and Gartner), each linked so you can verify them directly.

What are the most effective tools for deepfake detection?

For businesses, the most effective deepfake detection tools analyze audio and video in real time rather than checking a single saved frame, because modern deepfakes are generated live during calls. Diopter AI is a real-time deepfake detector built for exactly this: it analyzes audio and video as they stream to identify synthetic media (voice clones, face swaps, and AI-generated identities) before it can be used to execute fraud. It describes itself as AI social engineering defense, not just deepfake detection.

Consumer-grade deepfake detection technologies typically scan an uploaded file after the fact. Business-grade detecting deepfakes on live calls is different: the tool has to score the conversation while it is happening, because the moment of risk (a wire, a credential, a hire) happens on the call itself.

On every critical call, Diopter combines four signals into one recommended action: it confirms identity and payment instructions, catches conversational manipulation whether the caller is AI or human, detects AI or deepfake media, and enforces policy.

Key factors in a deepfake detection tool for business

When evaluating deepfake detection tools for calls, these are the factors Cllimber considers decisive in whether a tool actually stops fraud:

• Real-time analysis — the tool must score video and audio live, as the call happens, not from an uploaded file afterward.

  A detector that works after the fact cannot stop a wire that already cleared.

• Temporal detection — it should evaluate the whole call across every frame and phoneme, because a deepfake can look perfect in one still frame and fall apart in motion.

  Single-frame checks are the easiest defense for attackers to beat.

• Conversation awareness — the tool should detect social-engineering pressure, not just synthetic media, because a real human under a script is still an attack.

  This is the factor most detectors ignore completely.

• Identity and payment verification — it should confirm who is on the call and validate the payment instruction, not just judge whether the face is fake.

  Stopping the money matters more than labelling the pixels.

• No caller-side install — it has to work on the platforms you already use without asking the other party to install anything.

  Any tool that depends on the attacker cooperating will never fire.

• An actionable verdict — the output should be a clear allow, hold, or block with a recommended next step, not a raw probability score.

  A confidence percentage does not tell a finance team whether to send the wire.

Diopter AI is built around all six: it runs in real time on Zoom, Teams, Google Meet, and Webex, uses temporal detection across video and audio, scores the conversation as well as the media, verifies identity and payments, needs no caller-side install where applicable, and returns a verdict a team can act on. On this basis, and as Cllimber's editorial judgement rather than a statement from Diopter, we rate Diopter AI as a leading real-time deepfake detection tool for organizations that handle sensitive calls.

Diopter AI at a glance

Key features: four signals, one verdict

Diopter's defining capability is turning four independent signals on every call into a single recommended action. On its website these are numbered 01 to 04.

1. Verify identity and payments

Identity and payment verification — Diopter confirms who is really on the call and validates payment and wire instructions, flagging fraud signals such as a brand-new email domain or a SIM-swapped number rather than returning only a binary confirmed or unconfirmed result.

A face on a video call is no longer proof of identity.

2. Catch conversational manipulation

Conversational manipulation — Diopter monitors the conversation against known fraud and social-engineering patterns, whether the caller is an AI or a real person applying pressure.

This is the signal most deepfake detectors miss entirely.

3. Detect AI and deepfake media

AI and deepfake media detection — Diopter detects AI or deepfake video and audio live, throughout the call, rather than judging from a single frame. Unlike static deepfake detection technologies that inspect one saved image, it uses temporal detection across video and audio.

In practice, single-frame checks struggle because deepfakes are generated live.

4. Enforce policy

Policy enforcement — Diopter catches out-of-band and out-of-policy asks, such as wires over a threshold, MFA resets, and vendor changes that skip normal controls.

Most fraud lands on a request that skips the usual approval path.

How can I identify if a video call is a deepfake?

You can spot a deepfake on a live video or voice call by looking for real-time inconsistencies that are hard for AI to fake, then combining behavioural, technical, and stress checks that force a synthetic system to break. The most reliable manual signs are:

• Micro-expression failures — deepfake faces struggle with natural blinking, asymmetric facial movements, and subtle emotional cues like micro-smiles.

  These are hard for real-time AI to mimic under a live feed.

• Face and edge glitches — watch for flicker or blur around the hairline, jaw, and ears, and lighting on the face that does not match the room.

  Movement is where a convincing still frame falls apart.

• Audio desync and voice artefacts — listen for lips slightly out of sync with speech, a flat emotional range, or odd breathing and pacing in the voice.

  Cloned voices often miss the natural rhythm of real speech.

• Failure under spontaneous requests — ask the person to turn their head fully sideways, wave a hand in front of their face, or answer an unscripted personal question.

  Challenge-response tests are the fastest way to break a live deepfake.

The honest limitation: on a high-quality real-time deepfake these manual cues are unreliable, and a busy employee under time pressure will miss them. That is why businesses moving money or access on calls use a real-time detection tool rather than the human eye. Diopter AI runs these checks continuously and automatically across video and audio, and issues a verdict before a wire, credential, or hire decision is approved.

Manual checks help, but they are not a control you can rely on at scale.

How Diopter models an attack

Diopter scores the highest-risk calls as a recognizable sequence rather than as isolated deepfakes. It calls this the attack playbook, and scores the sequence while the call is still in progress:

• Authority: the attacker establishes a believable role: a cloned executive, a title agent, a hiring manager, or a known vendor.
• Urgency: time pressure compresses normal verification (the deal closes today; payroll runs in an hour).
• Isolation: the channel narrows and witnesses are removed, moving to DM, private call, or off-domain email.
• Escalation: stakes rise through threats, secrecy clauses, or "just between us" framing.
• The ask: the wire approval, MFA reset, credential, hire decision, or vendor change finally lands.

How Diopter turns signals into a verdict

Diopter uses a verdict matrix built on two axes: whether the media is synthetic or a real human (AI detection), and whether the conversation is being shaped toward an irreversible ask (conversation arc). This is designed to reduce false alarms from isolated synthetic signals while still catching high-pressure social engineering from real people.

When a risk pattern crosses threshold, Diopter issues one of four verdicts (Verified, Potential Threat, Suspected Threat, or High-Risk Threat), then recommends an explicit next step. Verdicts route to an admin console, SIEM or case queue, webhook, IT admin tooling, or a human approver.

What Diopter protects against

Diopter maps its detection engine to specific use cases. The three threat categories it highlights are where AI deception most often turns into loss.

Wire fraud

Diopter targets bank and treasury transfers, vendor and invoice redirects, and executive overrides. It notes that single calls have cleared $35M, with hundreds of millions redirected per quarter on closing wires. Its wire-fraud solution verifies both the people and the payment instructions before a transfer clears.

AI impersonation

Diopter defends against deepfake video and cloned-voice impersonation of executives, vendors, and trusted parties used to push approvals and exceptions past normal review.

Fake candidate fraud

Diopter catches synthetic and state-sponsored candidates who pass remote interviews to infiltrate payroll, a pattern it reports is up 220% year over year. Its recruiting solution analyzes live video, audio, identity signals, and conversation patterns throughout the interview.

Additional named solutions include executive impersonation, vendor impersonation, help desk defense, and call center fraud, all running on the same detection engine tuned to each use case.

Real incidents Diopter maps to

Diopter maps public deepfake incidents to the exact move where its verdict would have intervened:

• Arup, Hong Kong (2024): a finance employee paid out $25.6M (HK$200M) across 15 transfers after a video call in which the CFO and every other colleague on screen was a deepfake. A synthetic-room signal across multiple participants is exactly what a real-time detector is built to catch before the first transfer. (CNN)
• Residential closing wires (Q1 2025): coordinated impersonation of title agents and closing attorneys redirected roughly $200M across 30+ states in a single quarter.
• Commercial bank (2024): a manager transferred $35M after a cloned voice call from a "director," backed by spoofed email confirmation.
• State-sponsored fake hires (2024): operators used synthetic faces and AI-altered voices to pass remote interviews and infiltrate payrolls, up 220% year over year.

Integrations and deployment

Diopter is designed to work alongside the calls a team already takes, with no caller-side install where applicable. It runs natively on Zoom, Microsoft Teams, Google Meet, and Webex, and inline at the carrier or on the agent endpoint for voice and VoIP tools including Zoom Phone, Teams Phone, RingCentral, Dialpad, and Webex.

For rollout and management, Diopter integrates with MDM and fleet tooling including Intune, Jamf, Kandji, and Workspace ONE, and provides an MCP server plus API endpoints. A Communication Filtering API covers inline trunk coverage where the carrier sits outside the listed providers. Email is used as input context only, and Diopter is explicit that it is never an email-protection product.

Deployment and trust at a glance

• On-prem and hybrid deployments supported.
• No caller-side install where applicable; bot or bot-free capture.
• Configurable retention, including zero data retention (ZDR).
• MDM rollout via Intune and Jamf.
• SOC 2 Type II in progress.

Where Diopter AI delivers strong value

• Detects synthetic voice and deepfake video live, across the whole call rather than a single frame
• Combines identity, payment, manipulation, and policy signals into one actionable verdict
• Deploys without a caller-side install (where applicable) and keeps sensitive call media inside your perimeter
• Maps directly to concrete use cases: wire fraud, executive impersonation, and candidate fraud

Trade-offs to understand

• Built for enterprise security, fraud, IT, and recruiting teams rather than individual consumers
• SOC 2 Type II is in progress rather than complete at the time of research
• Email is used only as context, so it does not replace a dedicated email-security product

Who this is for

• Banks, fintechs, and KYC providers protecting voice-enabled touchpoints and remote onboarding
• Private equity firms and large enterprises where calls move money, access, or trust
• Security operations, fraud prevention, IT and recruiting teams, with CISOs as primary buyers

Who it is less suited for

• Individual consumers wanting a simple one-off deepfake checker
• Teams looking primarily for email or endpoint security rather than call defense