Show HN: Is this photo edited? Client-side image forensics

Isn’t this kind of Claudeslop something you could at least remove from the homepage of a product like this? What does this even tell me? Nothing! It tells me the person who created this site had no opinion about what it should actually do, and just let their LLM agent building something that vaguely fits their impression of what could be done to detect image tampering.

It found the "AI Generated" signature embedded in it by the creator (whatever image model Perplexity decided to use.)

But I wouldn't have called this image AI. So images that have merely been color corrected or other very minor changes may come up as "AI Generated" even if they're essentially real images.

Many other techniques here are more or less quack science with some sordid history. If I recall correctly, the entire field was invented by a guy of questionable integrity. Most of these methods are riffs on "if you download a badly compressed JPEG, paste some higher-quality content into it, and then save it at high quality setting and the same resolution, it's going to show if you run a highpass filter on the result". This is not wrong, but commonly fails on innocent images and it's also not how professional photo editing is done.

Tools that aren't this seem even more questionable. The "clone detector" triggers on anything that naturally shows even a bit of symmetry or repetition. The "noise comparison" tool detects bokeh and smooth surfaces. Etc.

TL;DR: Experiment with it, but don't assume that just because it sounds scientific, it actually works in real life.

Content Credentials (C2PA) are parsed from the JUMBF boxes and verified with WebCrypto: COSE_Sign1 signature (ECDSA P-256/384/521, RSA-PSS), each assertion hashed against the signed claim, the file hash binding, and the certificate chain walked to a vendored copy of the C2PA trust list. Not checked: revocation, signing-time timestamp, key-usage policy.

Pixel side: quantization tables against the libjpeg standard set, double-quantization periodicity in the DCT histograms, JPEG ghosts, error level analysis, noise map, block-based copy-move. Two heavier ones run on demand. A transformed clone search: FAST-9 corners with Harris ranking spread over 64 px cells, 8-level pyramid, 256-bit steered BRIEF, similarity RANSAC binned by angle and scale, a second pass against the mirrored image, patch correlation after the fit. A local noise comparison: trimmed mean of the Laplacian residual per 32 px tile against tiles of the same brightness and texture class elsewhere in the image. Two versions of a photo can be compared; alignment tries correlation first, then the feature matcher, so crops, resizes and rotations line up.

Limits: no AI detection claims, because without credentials nothing reliable exists. The two on-demand methods are experimental and unscored: tuned on planted edits, not yet measured on a real-photo corpus. Repeated real objects match like clones, and the card says so. 24 MP cap, 16 MP for the noise comparison. Findings are things to look at, not proof either way. Vanilla JS, no framework, no CDN.

One of the client-side tools on vajba.com, built with Claude Code. No ads, no tracking.