Forensics Toolset

ELA re-saves the image as JPEG and measures per-pixel differences between the original and the re-saved version. Every JPEG save degrades quality uniformly — regions that were edited or composited from a different source have a different error baseline and appear as brighter patches in the heatmap.

What to look for: Brighter or more saturated patches on smooth surfaces (faces, walls, clear sky); uneven brightness across a large uniform area; sharp-edged bright rectangles marking a paste boundary; JPEG ghosting — faint double-edges around pasted objects.

False positives and limitations: Text, sharp edges, fabric, foliage, and cartoon art produce high ELA naturally. Images saved multiple times at the same quality "even out" and show falsely low ELA. Screenshots and social-media re-encodes produce uniform high noise across the whole image, making them nearly unreadable. Never use ELA as the sole basis for a conclusion.

Further reading: FotoForensics ELA tutorial · 29a.ch Forensically (third-party) · Bellingcat: Forensically guide (third-party — Bellingcat caution: never rely on ELA alone)

Identifies copy-move forgery by finding visually similar regions at different positions. The image is divided into overlapping 16×16 pixel blocks; feature vectors are built and sorted, then nearest-neighbour pairs are flagged as potential clones and highlighted with colour-coded overlays.

What to look for: Matching colour-coded boxes far apart in the image — same colour means the same matched pair. Long connecting lines between distant regions are the most suspicious. Watch for repeated textures repositioned to fill gaps: sky patched in, crowd backgrounds duplicated, objects removed by cloning a nearby region over them.

False positives and limitations: Sky, walls, solid-colour fills, and smooth gradients are legitimately similar and generate noise — especially at Medium/High sensitivity. Does not detect AI inpainting, diffusion-based content-aware fill, or frequency-domain edits. Start with Low sensitivity and increase only if specific suspicious regions warrant closer inspection.

Further reading: 29a.ch Forensically (third-party) · Bellingcat: Forensically guide (third-party)

Reverse image search checks whether this image has appeared elsewhere on the web, and in what context. It is often the fastest way to determine whether an image is misrepresented — showing a photo from a different event, location, or date than claimed.

What to look for: Earlier appearances of the same image; original source or photographer credit; different captions or contexts in prior uses; signs the image has been cropped or flipped to evade detection.

Privacy notice: Unlike the other tools in this toolset, reverse image search sends your image to an external server. Each service will prompt for confirmation before uploading. Do not use with unpublished or confidential images.

Re-encodes the image through the browser canvas API, which discards all embedded metadata bytes. The output is a clean JPEG with no EXIF, IPTC, or XMP.

Use cases: Remove GPS location before sharing; prepare a clean file for publication; preserve only copyright fields for attribution.

Limitations: Canvas re-encoding introduces a small quality reduction, controlled by the quality slider. "Keep date" re-injects DateTimeOriginal only (via piexifjs) — all other fields including GPS are removed. "Keep copyright" preserves Artist, Copyright, and IPTC byline fields only; everything else is stripped.

Detects noise patterns and compression artifacts by comparing each pixel to its immediate neighbours. A 3×3 median filter is applied to the greyscale image; the difference between each pixel and its local median is its noise residual. High residuals appear red in the heatmap, low residuals appear blue.

What to look for: A patch with visibly different noise texture from its surroundings — unusually smooth (denoised, airbrushed) or unusually noisy (composited from a different camera or ISO setting). Rectangular or edge-aligned noise boundaries suggest a region was copied from a different source. A face smoother than the surrounding hair and clothing suggests skin retouching.

False positives and limitations: Clear sky, studio backdrops, and solid-colour areas always show low residuals (blue). Fabric, foliage, and hair always show high residuals (red) — this is natural. JPEG compressed at high quality produces very low residuals throughout. Two cameras with similar sensor noise composited together may not produce a visible noise boundary.

Draw directional arrows on the image to map observed shadow and light directions. All shadows in a physically consistent outdoor scene must point away from a single light source — disagreement between vectors suggests compositing or manipulation.

How to use: Load an image, then click and drag on the canvas to draw arrows tracing shadow edges or light ray directions. Each vector's angle (0–360°) and pixel length are logged in the list below the canvas. Use ↶ Undo to remove the last vector, or Clear all to start over.

What to look for: Vectors pointing in significantly different directions indicate different light sources — a strong indicator of compositing. Vectors that nearly agree but differ by 10–15° may indicate elements photographed at different times of day. Mismatched shadow lengths relative to object heights suggest elements from different times or locations.

False positives and limitations: Multiple artificial light sources (studio lights, streetlights, windows) legitimately produce multiple shadow directions. Curved or tilted surfaces create shadows that don't follow a simple directional model. Perspective distortion can make parallel shadows appear to converge. Accuracy depends entirely on correctly identifying and tracing shadow edges.

Further reading: SunCalc.org — verify sun angle for a given location and time · Bellingcat's Online Investigation Toolkit (third-party — some tools require upload or account)

Computes the sun's compass bearing (azimuth) and height above the horizon (altitude) for a given date, time, and location — entirely in your browser, using the same low-precision solar position formulas as public sun-position calculators. No image data or location is sent anywhere.

How to use: Date, time, UTC offset, and GPS coordinates auto-fill from the image's EXIF data when present — all fields are editable so you can test alternative claimed locations or times. To check consistency against a shadow, first draw a vector on the Shadows tab tracing the shadow's direction, then select it here and enter the camera's compass heading (auto-filled from EXIF GPSImgDirection when the device recorded one).

What to look for: If the sun is below the horizon (altitude ≤ 0°) at the claimed date/time/location, no direct shadow could exist — a strong red flag. If a shadow vector and camera heading are supplied, a large delta between the sun's predicted shadow bearing and the shadow's derived bearing suggests the claimed location, time, or camera direction is inconsistent with the image.

False positives and limitations: Converting an in-frame shadow angle to a true compass bearing assumes the camera points at the entered heading toward the top-centre of the frame with a roughly normal lens — this is an approximation. It degrades for shadows near the frame's edges, wide-angle or fisheye lenses, tilted cameras, and sloped ground. Treat deltas under ~15° as consistent, 15–30° as inconsistent-but-within-approximation-error, and only larger deltas as a meaningful discrepancy. This tool narrows down consistency — it cannot independently prove a location on its own; cross-reference with the Weather History and SunCalc tabs and other sources.

Further reading: SunCalc.org (third-party — cross-check candidate locations visually) · Bellingcat's Online Investigation Toolkit (third-party)

Checks for three classes of provenance signal that can be verified entirely in your browser — no data is uploaded anywhere.

C2PA / Content Credentials: The Coalition for Content Provenance and Authenticity open standard embeds a cryptographically signed manifest into image files. It records which tools touched the image and whether AI was involved. Adobe Photoshop, Lightroom, Firefly, Nikon Z-series cameras, Sony α-series, and Microsoft tools all embed C2PA. Detection here confirms presence only — cryptographic verification of the signature chain requires an external validator.

IPTC Digital Source Type: The IPTC Photo Metadata Standard includes a DigitalSourceType field that declares whether an image is an original photo, AI-generated, or a synthetic composite. Adobe tools write this field automatically for AI-generated content. Like all metadata, it can be edited or removed.

AI generator fingerprints: Known AI tool names and signatures in EXIF/XMP software, creator, and history fields.

False positives and limitations: All three checks rely on declared or embedded metadata that can be stripped, fabricated, or simply absent. A clean result does not confirm authenticity. Authentic news photographs rarely carry C2PA. Most AI-generated images in circulation have had metadata stripped before distribution.

What cannot be checked locally: SynthID (Google DeepMind) and other invisible pixel-level watermarks require their maker's proprietary detector models — they cannot be verified in a browser. See the Verify Elsewhere ↗ tab for links to providers' own verification tools. Their absence tells you nothing.

Further reading: Content Authenticity Initiative · IPTC Photo Metadata Standard · Bellingcat toolkit (third-party — some tools require upload)