Chromatic Aberration Analyzer
Analyze lens chromatic aberration patterns across the image. Consistent CA suggests a single camera. Inconsistent patterns may indicate compositing or splicing from different sources.
1,500+ images analyzed for CA
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Lens Fingerprint
Every lens produces a unique chromatic aberration pattern — the way red, green, and blue light focuses at slightly different points. This acts as a fingerprint for the camera-lens combination that captured the image.
Splice Detection
When parts of an image come from different cameras, the CA patterns break. Composited regions show different color fringing characteristics than the rest, revealing the splice boundary.
Web Worker Processing
Heavy pixel analysis runs in a background Web Worker thread, keeping the browser responsive. Adjustable grid resolution lets you balance analysis detail against processing time.
What is chromatic aberration?
Chromatic aberration (CA) is a lens defect where different wavelengths of light refract at slightly different angles, causing color channels to misalign at high-contrast edges. This appears as colored fringing — typically red/cyan (lateral CA) or purple/green (longitudinal CA). Every lens produces a characteristic CA pattern based on its optical design, focal length, and aperture. Forensic analysis uses this property as a camera fingerprint: consistent CA across an image suggests it was captured by a single lens, while inconsistent patterns may indicate compositing. For complementary analysis, try the ELA Scanner or Noise Analysis.
How CA reveals image splicing
When a forger pastes a region from one photo into another, the inserted region carries the CA characteristics of its original camera. If the source and target cameras have different lenses, the CA patterns will clash — one region might show strong lateral fringing while the surrounding image shows minimal aberration. This tool divides the image into a grid and measures CA vectors per block, flagging regions where the pattern deviates from the overall trend. For broader manipulation detection, combine with the Authenticity Checker and Clone Detection.
Limitations and false positives
CA analysis is one signal among many — it should not be used as the sole indicator of manipulation. Some legitimate causes of CA inconsistency include zoom lenses at varying focal lengths across the frame, in-camera CA correction that was only partially applied, heavy JPEG compression that destroys subtle color offsets, and flat regions with no edges (where CA cannot be measured). Always cross-reference with other forensic tools. Check for compression artifacts with JPEG Ghost Scanner or view the underlying data with the Bit Plane Viewer.
Grid-based analysis method
The tool divides the image into a configurable grid (6×6 to 12×12) and analyzes each block independently. Within each block, it detects edges using the Sobel operator, then measures the offset between red-green and blue-green gradient vectors at those edges. Blocks with similar CA vectors are consistent (green on the heatmap). Blocks that deviate significantly appear yellow or red. A finer grid provides more spatial resolution but requires more edge pixels per block to be reliable. For images with few edges, use a coarser grid. Explore more forensic tools at Scanly's full toolkit.
All analysis runs entirely in your browser via a Web Worker thread — your images are never uploaded to any server. The heatmap overlay is generated in real-time and can be downloaded as a PNG. For checking image metadata before forensic analysis, start with the EXIF Checker. Learn more in our articles on detecting edited photos and image forensics guide.