Fakeradar
Fakeradar · Video · AI Detection
Fakeradar is a browser-based deepfake detection tool that tells you whether a video was made by AI or shot on a real camera. You upload a clip up to 15 MB, the system analyzes frame-by-frame changes and motion patterns, and it returns a simple verdict: AI-generated or original. It's built for anyone who needs to detect AI-generated video fast, from journalists to HR teams reviewing candidate interviews, and it runs in beta with a free basic check. No app. No account.
About Fakeradar
What Is Fakeradar
Fakeradar is an AI video detector built by a team focused on one problem: making the origin of a video measurable. Instead of asking you to judge a clip by eye, it looks at spatiotemporal anomalies and behavioral inconsistencies that show up when a neural network generated or altered the footage.
The tool draws a clear line between two things people often confuse. Videos taken with a real camera count as original, even if they've been run through HDR, noise reduction, color correction, stabilization, or AI quality enhancement. Fully synthetic videos, deepfakes, AI avatars, face replacements, object restoration, and synthetic inpainting all get flagged as AI intervention. If any part of the scene was created or altered by a neural network, the whole clip is labeled AI-generated. No exceptions.
The biggest limitation is scope. Fakeradar handles video only, so it won't check a still photo or an audio clip. It also won't hand you a certified forensic report; it's built for fast, real-time analysis rather than formal expert conclusions. And no detector is perfect, so the team is upfront that 100% accuracy isn't guaranteed. Worth knowing before you rely on it.
Getting Started
- Open fakeradar.io in any browser. There's nothing to install.
- Drag your video file onto the page or pick it manually. MP4, MOV, and other common formats work up to 15 MB.
- Choose the Basic Check, which scans for signs of generative AI and returns a verdict.
- Read the result. The system marks the clip as either "we consider it a fake" or "we consider it original."
- For teams that need tighter control, ask about on-premise deployment inside your own infrastructure.
Product Information
A quick look at Fakeradar's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Journalists and editors
- HR and recruiting teams
- Trust and safety staff at platforms
Tasks
- Checking a viral clip before you share it
- Vetting a video interview
- Reviewing media sent by sources
Scenarios
- Verifying a breaking-news video during a fast news cycle
- Screening insurance or banking claim videos
- Auditing dating-profile videos
Key features
Real-Time AI Video Detection
Fakeradar analyzes patterns inside a video rather than relying on a database of known fakes. It looks at frame-to-frame changes, motion consistency, and the artifacts AI generators tend to leave behind. That approach lets it flag content from both existing and newly released models, not just the ones it was trained on. It also means no catalog update is needed to spot a fresh model.
Handles Full Fakes and Partial Edits
The detector covers the whole range of AI intervention. Deepfakes, fully generated videos, AI avatars, neural network face replacement, object restoration, and synthetic inpainting all trigger a fake verdict. The bar is strict: if any part of the scene came from a neural network, the entire video is labeled AI-generated. That's the point.
Doesn't Punish Normal Camera Processing
Here's where Fakeradar differs from tools that flag anything touched by software. A clip shot on a smartphone, webcam, or DSLR still counts as original, even after HDR, noise reduction, color correction, stabilization, or AI quality enhancement. The logic is that built-in camera processing isn't the same as generative AI. Not even close.
No Storage, No Recording
Your videos are processed in real time and aren't stored, and the company says it doesn't share your data with third parties. For teams handling sensitive footage, like a legal matter or a private interview, that matters. Uploaded clips and results don't sit on a server afterward.
Browser-Based, Nothing to Install
You don't download an app or set up an account just to run a check. Open the site, drop in a file up to 15 MB, and get a result. That low barrier is a big part of the appeal when you need an answer in the middle of a workflow.
API and On-Premise Deployment
For companies that want checks inside their own pipelines, Fakeradar offers API integration and on-premise deployment within your infrastructure. That fits editorial and moderation systems where uploads to a third-party site aren't allowed, and it's the path most enterprise buyers will want. No cloud round-trip required.
Built for Trust-Sensitive Industries
The tool is aimed at places where a fake can do real damage: banks, insurance companies, media organizations, HR teams, and dating platforms. Each of these handles identity or evidence, so a quick authenticity check fits naturally into review and approval steps.
Pros and cons
Pros
- Works right in the browser with no install, so you can run a deepfake check in seconds.
- Flags both fully generated video and partial AI edits, so most manipulation types get caught.
- Leaves normal camera processing alone, which avoids false alarms on everyday footage.
- Doesn't store uploads or results, a plus for anyone handling sensitive clips.
- Offers API and on-premise options for teams that need checks inside their own systems.
Cons
- Video only. You can't run a still image or an audio file through it.
- The 15 MB upload limit rules out longer or higher-resolution files without splitting them first.
- No certified reports, so it works for a quick call but not for formal legal or forensic use.
- It's in beta, with the Extended Check still marked as "coming soon," so features can shift.
Frequently asked questions
It analyzes a video and sorts it into one of two buckets: AI-generated or original. You upload a clip, and it studies frame-to-frame changes, motion consistency, and AI artifacts to decide which label fits. Simple as that.
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