Error Level Analysis (ELA)

ELA detects image manipulation by re-compressing a JPEG at a known quality level and comparing it to the original. Authentic images show uniform error levels across similar surface types. Edited regions typically show different compression artifacts and appear brighter in the ELA visualization, indicating they were saved at a different compression level — a sign of manipulation.

EXIF Metadata Analysis

EXIF data is embedded in JPEG and TIFF files by cameras and phones. It contains: camera model, GPS coordinates, date/time, lens settings, and software used. Removed or inconsistent EXIF data (e.g., no GPS on an outdoor photo, or editing software tags) can indicate the image has been processed. Entirely missing EXIF is common in screenshots or web-downloaded images.

AI Authenticity Detection

The AI model analyzes pixel-level patterns, lighting consistency, shadow geometry, face symmetry, and texture coherence. AI-generated images (deepfakes, GAN outputs, diffusion models) often show subtle artifacts in hair, teeth, backgrounds, and reflections that the model has been trained to detect.

Interpreting Results

No single indicator is definitive. A high ELA anomaly score with inconsistent EXIF and low AI authenticity together strongly suggest manipulation. But high ELA can also occur with legitimate heavy re-editing for artistic purposes. Treat the analysis as probabilistic evidence, not forensic proof. Always corroborate with other signals and context.