Everything you need to know about how Fake Image Detector works.
Upload a JPG, PNG, BMP, TIFF, or WebP image under 10 MB using the scanner on the home page, then press Scan Now. The app will run Metadata Analysis and ELA Analysis and show you the results instantly.
Given hardware constraints and the relatively focused training dataset, accuracy currently sits between 60 and 70 percent. Accuracy can be improved by expanding the training data fed to the LBPH model.
Yes — the tool is designed for users of all technical levels. However, users should understand its limitations and not treat results as legally conclusive evidence.
ELA is a digital image forensics technique that highlights inconsistencies in an image's JPEG compression levels. Regions that have been edited or composited often show a different error level than the rest of the image.
The Local Binary Patterns Histograms (LBPH) recogniser — originally designed for facial recognition — is used here to compare image histograms against a trained dataset of authentic and manipulated images, adding a machine-learning layer on top of raw ELA.
Metadata Analysis inspects the EXIF data embedded in a JPEG file. When an image is edited with software like Photoshop or GIMP, those tools often leave behind a software signature in the metadata that can be detected.
Accuracy improves with a larger and more diverse training dataset, better model tuning, and additional forensics techniques. Admins can upload new .yml training files directly through the admin panel.