Free Deepfake Detector

How Deepfake Detection Works

Deepfakes are AI-generated or AI-manipulated media designed to replace one person's likeness with another. Our deepfake detector analyzes multiple forensic signals to identify these manipulations. GAN fingerprinting detects the mathematical artifacts left by face-swap architectures like DeepFaceLab and FaceSwap. Our temporal analysis engine examines frame-to-frame consistency in videos, catching the flickering and warping that deepfake models introduce. PRNU analysis checks for the camera sensor noise patterns that real video footage contains but synthetic frames lack. Facial geometry analysis identifies anatomical impossibilities in eye reflections, ear structure, and teeth rendering that current AI models struggle to reproduce consistently.

Why Deepfake Detection Matters

Deepfake technology has moved from research labs to consumer apps, and the financial damage is accelerating. Deepfakes are used in CEO fraud schemes, romance scams, political disinformation, non-consensual intimate imagery, and identity theft. The rise of real-time deepfake filters means that even live video calls can no longer be trusted at face value. A reliable deepfake detector is essential for journalists verifying sources, HR teams conducting video interviews, dating app users checking profile authenticity, and anyone who needs to verify that a person in a photo or video is real.

Deepfake Detection for Video

FauxLens supports deepfake detection in both images and video files. For video analysis, our engine extracts key frames and analyzes each for signs of AI generation or face-swap manipulation. We detect content from Sora by OpenAI, Veo by Google, Kling by Kuaishou, Runway Gen-3, and other AI video generators. Upload MP4 or MOV files up to 100MB for thorough forensic analysis including temporal consistency checking, audio-visual sync analysis, and per-frame artifact detection.

Real-Time Deepfake Filters: The New Frontier of Live Call Fraud

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.

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 analyzes recorded video from calls - a screen recording of a Zoom session, for example - and identifies these real-time filter artifacts. 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.

Deepfake Detection Use Cases by Industry

Deepfake detection has become operationally necessary across multiple industries, each with different threat profiles and stakes.

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 detects face-swap video with 97.5% accuracy on recorded interview clips.

Journalism: 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.

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: Courts in multiple US jurisdictions have encountered AI-fabricated photographic evidence submitted in civil 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.

How to Protect Yourself From Deepfake Fraud

Protecting yourself from deepfake fraud requires both detection tools and behavioral practices, because no single countermeasure is sufficient on its own.

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 fool the human eye more than 70% of the time.

For job candidate verification: record video interviews and submit clips 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.