Noise Analysis Scanner
Reveals manipulation traces by extracting the noise pattern from an image. Edited regions show inconsistent noise levels — airbrushing, warping, and clone stamping leave visible marks.
500+ images analyzed
Drag & drop a file here, Ctrl+V to paste, oror
Reverse Median Filter
Subtracts a median-filtered version from the original to isolate the high-frequency noise residual.
Block Analysis
Divides the noise map into blocks and compares variance across regions to detect localized manipulation.
100% Private
All analysis runs locally in your browser via Web Worker. Nothing is uploaded to any server.
How noise analysis works
Every digital photo contains sensor noise — a subtle random pattern created during capture. Noise analysis extracts this pattern by applying a median filter (which preserves edges but removes noise) and subtracting the result from the original image. What remains is the noise residual. In an unedited photo, this noise is distributed uniformly. Manipulation tools like airbrush, clone stamp, warp, and content-aware fill disrupt the noise pattern, creating visible inconsistencies. For compression-based detection, combine with the ELA Scanner. For copy-paste forgery, use Clone Detection.
Understanding the controls
The kernel size controls the median filter window. A 3×3 kernel captures fine noise detail but may include edge artifacts. A 5×5 or 7×7 kernel produces a smoother noise map that better highlights large retouched areas but loses fine-grained information. Amplification (1–50×) magnifies the noise residual for visibility. RGB mode shows per-channel noise in color; luminance mode converts to grayscale for easier pattern spotting. Histogram equalization stretches the contrast to make subtle differences more visible. Use the Re-run button to adjust parameters without re-uploading.
Who uses noise analysis
Photo editors and retouchers use noise analysis to quality-check their own work and ensure seamless blending. Forensic analysts detect airbrushing and beauty retouching in evidence photos. Academic integrity reviewers check scientific images in journal submissions for undisclosed manipulation. Insurance adjusters examine claim photos for suspicious smoothing or patching. For metadata-level verification, combine with the Authenticity Checker. For a comprehensive forensic workflow, visit the photo forensics hub.
Noise analysis vs ELA
ELA detects compression inconsistencies — it works best on JPEG images that have been through at least one compression cycle. Noise analysis detects texture-level manipulation regardless of format. ELA catches pasted regions with different compression history; noise analysis catches retouching, smoothing, and local noise reduction that ELA may miss entirely. These tools complement each other. For splice detection via JPEG quality levels, add the JPEG Ghost Scanner. For hidden data analysis, try the Stego Scanner.
Noise analysis is one of the most effective techniques for detecting subtle retouching that other forensic methods miss. Beauty retouching, skin smoothing, and selective noise reduction all suppress the natural sensor noise in affected regions, making them stand out clearly in the noise map. The block-based uniformity analysis automatically flags regions with anomalous noise levels. All processing runs entirely in your browser via a Web Worker — your images are never uploaded. For checking whether an image was AI-generated, use the AI Detector. For thumbnail-based editing detection, see the Thumbnail Scanner. For device-level identification via sensor noise patterns, try the Camera Fingerprint (PRNU) tool. Learn more in our articles on detecting photo edits and image forensics guide.