Local AI Image Detector — Chrome Extension

A Manifest V3 Chrome extension that detects AI-generated images with all inference running on-device (WebGPU, WASM fallback). No cloud, no APIs, no local server — no image data ever leaves the browser.

Built for the poidh "local AI challenge" bounty: ≥75% balanced accuracy at a fixed 0.65 confidence threshold, evaluated offline.

In action

Live on x.com and grok.com — red pill = judged AI at ≥65% confidence, small chip = score below threshold, blue "graphic" chip = charts/screenshots where a photo detector doesn't meaningfully apply:

Measured accuracy

89.5% pooled balanced accuracy @ threshold 0.65 on a 2,500-image evaluation that was deliberately built to be hard (details in eval/RESULTS.md):

• Fakes: OpenFake test split — 19 generators including sora-2, gpt-image-2, flux-2-klein, midjourney-7, z-image-turbo (2025–26 era), never trained on.
• Reals: six distributions — OpenFake reals, COCO, Pascal VOC, Wikimedia Commons quality images, Unsplash, and pre-2022 memes (meme-laundered photos: text overlays, heavy recompression — what social feeds are actually full of). FPR is uniform (0–14%) across all six.
• Everything web-realistically re-encoded (≤1024px, JPEG q85) identically for both classes so format cues can't leak.
• Out-of-distribution check: 99.6% recall on Community Forensics' own held-out generators — the fine-tuned head did not narrow to one fake distribution.
• AUC 0.959 · fake recall 87.2% at the 0.65 operating point.

Per-generator recall at the fixed 0.65 threshold (shipped model, held-out images):

False-positive rate on real photos, stock head vs shipped re-fit head:

* 6–7 eval images each — small-sample numbers.

Full methodology, ablations (graphic gate: −0.06pp on the benchmark), and every intermediate measurement: eval/RESULTS.md. This is our own proxy benchmark — built from sources disjoint from training and scored by eval/harness.py, reproducible end-to-end — not the bounty's private benchmark.

The model

Community Forensics ViT-S/16 @384 (MIT-licensed weights; Park & Owens, CVPR 2025, trained on 4,803 generators) with a re-fit classification head trained on frozen features to fix a measured blind spot: the stock head false-positives heavily on modern professional photography (42.6% FPR on Unsplash). The re-fit (weighted logistic regression, L2-regularized toward the original head, trained only on provably-real content: pre-2022 camera-EXIF Unsplash, Wikimedia Commons quality images, and pre-2022 memes) brings Unsplash to 13.7% and memes from 45.8% to 4.4%, while still raising fake recall over stock. A calibration offset (+1.67 logits) centers the optimal balanced-accuracy operating point exactly at the required 0.65 threshold.

Exported to ONNX (fp32, 87MB, committed in extension/models/detector.onnx) and executed by ONNX Runtime Web — WebGPU when available, single-thread WASM otherwise.

Install (Chrome or Brave)

Easiest — prebuilt release (no build tools needed): download the zip from Releases, unzip it, then chrome://extensions → Developer mode → Load unpacked → select the unzipped local-ai-image-detector folder. Done — no Node, no Python, nothing else.

From source:

git clone <this repo>
cd ai-image-detector/extension
npm install          # pinned onnxruntime-web 1.27
node build.mjs       # vendors ORT runtime + wasm into lib/ort/ (no CDN at runtime)

Then: chrome://extensions → enable Developer mode → Load unpacked → select the extension/ folder (the folder containing manifest.json). Click the toolbar icon and switch Detection on. See INSTALL.md for details and troubleshooting.

The model ships inside the repo — after git clone + npm install the extension is fully offline; it performs no downloads at any point.

What it does

• Analyzes images one at a time, viewport-first — the queue always takes the image nearest the top of your current viewport, so scrolling re-prioritizes naturally and the machine is never saturated.
• Every analyzed image gets a visible confidence score: a red pill (AI generated · 97%) at ≥65%, a small neutral chip (3%) below it. Hover for details. (The all-scores chips can be toggled off in the popup.)
• Works on real websites: single-page apps (x.com — injected into open tabs, survives SPA navigation), CSS background images, <picture>/srcset, lazy-loaded feeds, open shadow DOM, iframes, blob:/data: URLs, and strict-CSP sites (tags are styled via CSSOM, which style-src cannot block). Language-independent: the pipeline reads pixels and DOM structure, never page text (verified live on Japanese and Arabic pages).
• Knows what it doesn't know: charts, tables, and UI screenshots are detected by a pixel-statistics gate (97% of rendered graphics incl. dense multi-panel charts after social-media recompression, 2.5% of photos) and labeled "graphic" with reduced confidence instead of pretending a photo detector's score is meaningful there.
• Never re-analyzes: per-element, per-URL, and SHA-256 content-hash caches (the hash cache persists locally and recognizes the same image on any page; toggleable, clearable).
• Popup shows live session stats (analyzed / flagged / avg latency / cache hits), engine + backend, and the calibrated threshold.

Architecture

content script (per page)          background service worker       offscreen document
─────────────────────────          ─────────────────────────       ──────────────────
discover images (img/bg/shadow/    route + in-memory URL cache     fetch bytes (host-permission
iframe/blob), IntersectionObserver ensure offscreen doc            CORS-exempt), SHA-256 hash
viewport-priority queue (1 at a    session stats                   cache (storage.local),
time) → overlay score pills                                        decode → 440/384 preprocess →
                                                                   ONNX Runtime Web (WebGPU→WASM)

Reproducing the model from source

The shipped detector.onnx is deterministically rebuilt from public inputs:

pip install torch timm onnxruntime onnx datasets pillow numpy huggingface_hub
git clone https://github.com/JeongsooP/Community-Forensics vendor/Community-Forensics
cd eval
python export_features.py                 # backbone (HF: OwensLab/commfor-model-384, MIT) + original head
python bake_head.py --head models/head_384_refit_hi.npz --offset 1.67   # -> commfor_vits_384_refit.onnx

models/head_384_refit.npz (the 385-parameter re-fit head) is committed. To re-train it from scratch instead: eval/build_proxy.py, build_trainpool.py, fetch_unsplash.py, fetch_commons.py rebuild the datasets from public sources, then refit_head.py re-fits and reports; harness.py scores any model on any real/fake image tree. eval/RESULTS.md documents every measurement in this README.

Compliance notes (bounty rules)

• No cloud inference, no external APIs, no localhost/backend processes — the extension runtime consists of a content script, a service worker, and an offscreen document.
• No downloads after install (the model is in the package; ORT is vendored at build time).
• No benchmark hashes or lookup tables: the hash cache stores only the extension's own past outputs, keyed by content hash, as a performance optimization — it contains nothing at install time and can be disabled in the popup.
• Confidence score displayed for every analyzed image (default on).
• MIT license: this repo, the base model weights, and ONNX Runtime.

Credits

• Model: Community Forensics (Jeongsoo Park & Andrew Owens, CVPR 2025) — weights OwensLab/commfor-model-384, MIT.
• Runtime: ONNX Runtime Web (MIT).
• Evaluation data: OpenFake (ComplexDataLab), COCO, Pascal VOC, Wikimedia Commons, Unsplash Lite, Community Forensics Small — see eval/ scripts for exact usage.