ELA Scanner
Error Level Analysis reveals image manipulation by highlighting compression inconsistencies. Edited regions appear brighter in the ELA heatmap.
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Compression Analysis
Compares original vs re-saved JPEG to expose compression inconsistencies from edits.
Adjustable Controls
Tune JPEG quality level and amplification scale to fine-tune detection sensitivity.
100% Private
All analysis runs locally in your browser using Canvas API. Nothing is uploaded.
How it works
How ELA works
Error Level Analysis re-saves the image as JPEG at a known quality level, then computes the pixel-by-pixel difference between the original and re-saved version. Unedited regions that were compressed uniformly show similar error levels across the image. Manipulated areas — pasted objects, cloned regions, retouched faces — were compressed differently and produce noticeably higher or lower error values. The difference is amplified and displayed as a color heatmap where bright patches indicate potential edits. For double-compression detection that complements ELA, try the JPEG Ghost Scanner. For a metadata-based approach, see the Authenticity Checker.
Understanding the controls
The JPEG quality slider (85–99%) controls the re-save compression level. Lower values create more visible differences, making subtle edits easier to spot but also increasing noise from natural edges. The amplification scale (3–30×) magnifies the error signal — higher values reveal faint manipulation but may introduce false positives on textured or high-contrast areas. Start with the defaults (95% quality, 10× scale) and adjust based on what the image shows. For heavily compressed originals, try lowering quality to 90%. For high-quality originals, raise it to 98%.
Who uses ELA
Journalists and fact-checkers verify whether submitted photos have been manipulated before publication. Insurance investigators examine claim images for signs of doctoring — pasted damage, altered timestamps, or removed objects. Legal teams use ELA as part of forensic evidence review. OSINT researchers analyze images from social media to assess credibility. Content moderators flag potentially manipulated images on marketplaces and dating platforms. For a complete verification workflow, pair with the photo forensics hub.
ELA vs Authenticity Checker
The Authenticity Checker analyzes metadata — software tags, timestamp gaps, EXIF inconsistencies — to detect whether editing software touched the file. ELA analyzes pixels — compression patterns that reveal where visual content was altered. Metadata analysis is faster and catches software-level edits, while ELA catches pixel-level manipulation like clone stamping or object removal even when metadata has been stripped. For maximum forensic coverage, use both tools together. Add the AI Detector to check for fully synthetic images.
Error Level Analysis is one of the most widely used techniques in digital image forensics. It works because JPEG compression creates predictable patterns across an image, and any region that has been edited will have a different compression history from the surrounding pixels. While ELA is a powerful indicator, it is not definitive proof of manipulation — natural high-contrast edges, text overlays, and areas with fine detail will always produce elevated error levels. Treat ELA results as one signal among several. For related analysis, the Screenshot Scanner can determine if an image is a screen capture, and the Similarity Scanner can compare a suspect image against a known original to pinpoint specific alterations. All ELA processing happens entirely in your browser — no images are uploaded to any server. Read our ELA deep dive for tips, examples, and best practices.