https://fauxlens.com/assets/deepfake-detector-BLsCevkY.js

1(function(){try{var e=typeof window<"u"?window:typeof global<"u"?global:typeof globalThis<"u"?globalThis:typeof self<"u"?self:{};e.SENTRY_RELEASE={id:"6a4e691cf2be6b808c8876231f720214eb6faeef"},e._sentryModuleMetadata=e._sentryModuleMetadata||{},e._sentryModuleMetadata[new e.Error().stack]=(function(t){for(var i=1;i<arguments.length;i++){var a=arguments[i];if(a!=null)for(var n in a)a.hasOwnProperty(n)&&(t[n]=a[n])}return t})({},e._sentryModuleMetadata[new e.Error().stack],{"_sentryBundlerPluginAppKey:fauxlens-web":!0})}catch{}})();const o={slug:"deepfake-detector",title:"Free Deepfake Detector - Image & Video | FauxLens",metaDescription:"Upload an image or video and get a deepfake verdict in seconds. Free detector for face swaps and AI-generated faces. No signup, files deleted after analysis.",h1:"Free Deepfake Detector",heroSubtitle:"Upload a photo or a clip and FauxLens tells you whether it was made or altered with AI, and shows you the reasoning behind the call. You see what each check found, rather than a single number to take on faith. Free, no account, and the file is deleted after analysis.",toolMode:"detector",sections:[{heading:"How deepfake detection works",body:`A deepfake is a real face replaced by a generated one, and every method of doing it leaves something behind.
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3FauxLens starts with provenance. It reads content credentials and metadata, and checks for the invisible watermark OpenAI and Google embed in images their AI tools make. Then a specialist detection model scores the pixels and says whether the image looks real, ordinarily retouched, edited with AI, or generated by AI. A face swapped into a real photo is the kind of change the AI-edited result exists for, and tools like DeepFaceLab rarely leave credentials, so the pixel score does most of the work.
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5You can often see the blend yourself. A swapped face has to be warped onto a head that was never shaped for it, and the seam shows up as a mismatch between the face and the skull, ears and jaw around it. Ears are the classic giveaway: they belong to the original person, and they rarely match the new face.
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7Skin and light are the other places to look. A swapped face is often smoother or softer than the neck and hands in the same shot, and the light falling on it may not match the light on everything else. The face and the background can disagree about where the lamp or the sun was.
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9In video, watching frame by frame adds a dimension that still images do not have. Each frame is generated independently, so consistency between frames is something the model has to achieve rather than something it gets for free. Flicker at the blend boundary, jitter in facial landmarks, and lighting that shifts by a fraction between frames are all artifacts of that.
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11Every scan returns a verdict, a confidence score and the evidence lines behind it. When the evidence is mixed, the confidence shows it rather than hiding it behind a firm label, which matters because mixed evidence is usually the interesting part.`},{heading:"Why this got urgent",body:`The cost of making a convincing fake collapsed, and the cost of checking one did not.
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13Voice 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.
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15The 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 ran to 1.3 billion dollars in reported US losses in 2025. Deepfake-enabled fraud overall reached 12.5 billion.
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17What 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.
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19None of which makes detection a solved problem. It makes it a necessary one.`},{heading:"Detecting deepfakes in video",body:`Video is a harder target than a still, and a more informative one.
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21Upload 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.
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23Time 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.
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25Face-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.
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27Two 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.`},{heading:"Real-Time Deepfake Filters: The New Frontier of Live Call Fraud",body:`Real-time deepfake filters, tools that replace a person's face during a live video call, represent a fundamentally different threat than pre-recorded deepfake video. Applications like DeepFaceLive, Avatarify, and commercial products built on them can run on consumer GPUs and apply a face swap in under 30 milliseconds, well within the latency budget of a Zoom or Google Meet call. The victim sees what appears to be a real person speaking naturally.
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29Real-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.
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31FauxLens 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.`},{heading:"Deepfake Detection Use Cases by Industry",body:`Deepfake detection has become operationally necessary across multiple industries, each with different threat profiles and stakes.
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33HR 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.
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35Journalism: Deepfake images of public figures in fabricated situations are designed to create publishable controversy. In 2025, synthetic images of political figures were shared during two separate national elections before forensic teams identified them as AI-generated. Newsrooms integrating FauxLens into their photo intake workflow catch these before publication.
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37Banking 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.
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39Dating 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.
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41Insurance: 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.
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43Legal proceedings: Courts in multiple US jurisdictions have encountered AI-fabricated photographic evidence submitted in civ

43il cases. 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.`},{heading:"How to Protect Yourself From Deepfake Fraud",body:`Protecting yourself from deepfake fraud requires both detection tools and behavioral practices, because no single countermeasure is sufficient on its own.
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45For 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.
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47For 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 fool the human eye more than 70% of the time.
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49For 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.
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51If 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.`}],faq:[{question:"What is a deepfake?",answer:`A deepfake is AI-generated or AI-manipulated media, typically video or images, where a person's face, voice, or body has been synthetically replaced or generated. The term comes from "deep learning" + "fake." Modern deepfakes can be extremely convincing to the human eye.`},{question:"Can this detect deepfake videos?",answer:"FauxLens judges a video either from the whole file or from a set of sampled frames. It is built to catch face swaps, AI-generated faces, and fully synthetic video from generators like Sora, Veo, and Kling."},{question:"How do I know if a photo is a deepfake?",answer:"Upload the image to FauxLens. FauxLens reads the metadata and content credentials, checks for an OpenAI or Google watermark, and has a detection model score the pixels. You will receive a report with a verdict, a confidence score and the evidence lines behind it."},{question:"Is deepfake detection reliable?",answer:"No detector is perfect, and we do not publish a single accuracy figure to imply otherwise. What FauxLens returns instead is a per-finding breakdown - what was checked, what it found, and how much weight that carries - which is more useful than an averaged score because it shows you where the evidence is strong and where it is thin."},{question:"Can real-time video call deepfakes be detected?",answer:"Yes, but detection requires a recording of the call rather than live analysis during it. FauxLens can scan a screen recording of a video call like any other clip. While you watch, look for blurred edges around the face, skin that is softer than the rest of the frame, and odd blinking. For live calls, ask the person to perform spontaneous tasks like holding up a handwritten code - current real-time deepfake systems cannot handle unpredictable visual requests reliably."},{question:"What deepfake apps create the most convincing fakes?",answer:"In 2026, the most forensically challenging deepfakes come from DeepFaceLab (open-source, high quality but slow), commercial real-time filters built on FaceSwap architectures, and fully AI-generated faces from Midjourney v6 and Flux.1 Pro. FauxLens checks their output the same way: metadata and credentials first, then a detection model on the pixels. These tools can be caught, though heavy compression makes all of them harder to judge."},{question:"How do I report a deepfake to authorities?",answer:"For financial fraud involving deepfakes: file with the FBI Internet Crime Complaint Center at ic3.gov and the FTC at reportfraud.ftc.gov. For non-consensual intimate imagery deepfakes: report to the National Center for Missing and Exploited Children (NCMEC) and your state's attorney general. For political disinformation: report to the platform and to the Election Assistance Commission if it concerns election integrity. In all cases, preserve screenshots, recordings, and a FauxLens forensic report as documentation."},{question:"Can audio deepfakes be detected?",answer:"FauxLens specializes in visual media - images and video. Audio deepfake detection requires separate forensic tools that analyze voice synthesis artifacts, unnatural spectral characteristics, and prosody anomalies. For audio verification, specialized audio forensics platforms are required. FauxLens can analyze the video portion of calls where an audio deepfake may accompany a real face or a visual deepfake."},{question:"What is temporal analysis in video deepfake detection?",answer:"Temporal analysis examines consistency across video frames over time rather than analyzing each frame in isolation. AI video generators and deepfake tools often produce subtle flickering, warping, and physics violations between frames that are invisible when looking at any single frame but statistically detectable when comparing sequential frames. FauxLens has no dedicated temporal module; it judges a clip from the whole file or from sampled frames, and you can check continuity yourself by stepping through the clip slowly."},{question:"How do I know if my face has been used in a deepfake?",answer:"There is no reliable automated way to discover all uses of your face in deepfakes across the internet. The most practical approaches are: run reverse image searches of your photos periodically using Google Images and TinEye, set up Google Alerts for your name, monitor social platforms for unusual accounts using your photos, and use identity monitoring services that scan for lookalike accounts. If you find a deepfake using your likeness, report it to the platform under their synthetic media or impersonation policies."}],relatedBlogSlugs:["what-is-a-deepfake","detect-ai-generated-video-2026","romance-scam-warning-signs-2026","dating-apps-fighting-ai-profiles"]};export{o as default};