Free Deepfake Detector

Voice cloning now needs about three seconds of reference audio. Deepfake video can be produced for less than a dollar. Meanwhile verification still requires either expertise or a tool, and most people encountering a suspect image have neither to hand.

The documented damage follows the money. A finance employee at Arup authorised a 25 million dollar transfer after a video call in which every other participant, including the company's CFO, was synthetic. Romance scams built on AI profile photos are among the costliest frauds reported in the US, and deepfake-enabled fraud is growing with them.

What has changed most recently is latency. Real-time face swap runs on a consumer GPU inside the frame budget of a video call, which removes the last piece of advice everyone was giving: that you could trust a live conversation. You cannot, and the section below covers what to do about that instead.

None of which makes detection a solved problem. It makes it a necessary one.

Upload an MP4 or MOV up to 100MB. FauxLens judges the clip either from the whole file or from a set of sampled frames, and returns a verdict with a confidence score and the evidence behind it.

Time is where video-specific generators give themselves away. Sora, Veo, Kling and Runway Gen-3 each produce frames that are individually plausible and collectively slightly inconsistent, because temporal coherence is something the model approximates rather than obeys. Watch a generated clip frame by frame and you find edges that breathe, texture that reorganises between frames, and shadows that drift independently of the object casting them.

Face-swapped video fails differently. The underlying footage is real, so the background is temporally perfect while the swapped region is not. The mismatch between a stable scene and an unstable face is often visible if you step through the clip slowly.

Two practical notes. Heavily compressed video, anything that has been through a social platform more than once, loses fine detail and lowers confidence. And a short clip gives the scan less to work with, so a three-second video is a weaker test than a thirty-second one.

Real-time deepfakes are detectable but require different analytical techniques than static images or pre-recorded video. The most reliable signals are: edge artifacts around the face boundary where the synthetic overlay meets the real background, reduced facial texture resolution compared to the rest of the video frame, an absence of micro-expression variation that real faces exhibit subconsciously, and unnatural blink patterns - most real-time deepfake models struggle to replicate the precise timing of human blinking.

FauxLens can scan recorded video from calls (a screen recording of a Zoom session, for example) the same way it scans any other clip. For live detection during an ongoing call, the most practical countermeasure remains asking the other person to perform spontaneous tasks: hold a specific object, look directly left or right quickly, or read a randomly generated word from a sheet of paper. Current real-time deepfake systems cannot process these rapid, unpredictable requests reliably.

HR and hiring: The FBI issued a public warning in 2022 and updated it in 2025 documenting North Korea-linked operatives using deepfakes to fraudulently obtain remote employment at US technology companies. The goal is insider access to proprietary systems and source code. HR teams without deepfake screening in their video interview process are exposed to this threat. FauxLens screens recorded interview clips and profile photos for face-swap and synthesis artefacts, and returns the specific findings behind each verdict so a hiring team can see what was flagged rather than acting on a bare score.

Journalism: Deepfake images of public figures in fabricated situations are designed to create publishable controversy. Synthetic images of political figures have circulated around recent elections before forensic teams identified them as AI-generated. A newsroom that scans photos at intake catches these before publication.

Banking and KYC: Identity verification for financial account opening increasingly uses video selfies and liveness checks. Criminal organizations have developed AI tools specifically designed to defeat liveness detection with synthetic video. Banks adding a second-layer forensic check on submitted verification video reduce successful synthetic identity fraud attempts.

Dating platforms: Romance scammers generate consistent AI personas across multiple photos using Midjourney or Flux. These synthetic identities cannot be caught by reverse image search because they have never been photographed. FauxLens API integration at the photo upload stage gives dating platforms the ability to flag potential synthetic profiles before they reach other users.

Insurance: AI-generated damage documentation (fabricated photos of fire damage, vehicle accidents, and property destruction) is submitted to claims departments at scale. Insurers using forensic AI detection at the claim intake stage have reduced AI-fabricated claim payouts significantly.

Legal proceedings: AI-fabricated photographs are now cheap enough to be offered as evidence in civil cases, so they need checking before anyone relies on them. Several states have enacted rules requiring disclosure when AI tools were used in evidence creation, but detection capability is necessary when disclosure is not forthcoming.

For video calls and online relationships: establish a video-based liveness challenge before trusting any new relationship formed online or via professional platforms. Ask the person to hold up a handwritten code you provide in real time, or to quickly turn their face in a specific direction. Real-time deepfake systems cannot respond to arbitrary spontaneous visual tasks reliably. Request multiple unscheduled video calls at different times - deepfake actors frequently avoid or delay these.

For profile photos on dating apps and social platforms: upload the photo to FauxLens before emotionally investing in the relationship. AI-generated profile photos are the most common entry point for romance scams. Visual inspection is insufficient, the latest Midjourney and Flux outputs routinely fool the human eye.

For submitted-video verification: record the clip and submit it to FauxLens for forensic analysis. Cross-reference the candidate's stated identity against government-issued ID through a liveness-based identity verification service. Require a live unscheduled call in addition to any pre-recorded submission.

If you suspect you are the victim of deepfake fraud and money has been transferred: contact your bank immediately and request a transaction recall. File a report with the FBI Internet Crime Complaint Center at ic3.gov and with the FTC at reportfraud.ftc.gov. Save all evidence (screenshots, recordings, messages) before contacting the platform where you met the person, as account deletion can be rapid once a scammer knows they have been identified.