SlopGuard

An extension that detects most AI-generated images on web pages and visually flags them.

When you click the toolbar icon (the six-finger hand icon) on any page, SlopGuard runs every eligible image (rendered area ≥ ~200×200 px, images that are loaded on-screen) against a series of checks including:

• Metadata extraction
• Check for Google's SynthID invisible watermark
• A visual classifier (i.e. a local model that says an image looks like AI)

You can also right-click on any image to check just that one image.

AI-flagged images get a red AI / Probably AI / yellow Maybe AI label and are dimmed to grayscale; checked-clean images get a thin green outline; failed checks get a grey outline.

Everything runs locally — no remote API calls, no user-supplied keys. This means it's fast and private.

Visual Identification of AI images is a best guess, there will be misses and false positives

The two on-device detectors are based upon others' work, so thanks are due to: Victor Livernoche @ ComplexDataLab's OpenFake project for the general classifier.

fyxme's fyxme/opensynthid-detect-0.1 local SynthID surrogate.

HOW IT WORKS

Tiers 1–4 short-circuit on hit. Tiers 1 & 2 are declared signals (the file literally claims it's AI); tier 4 reads the SynthID watermark, treated as strong provenance. All four produce the red AI label.

Tier 5 fires when OpenFake crosses its detect threshold → red Probably AI if it also crosses the confident threshold, otherwise yellow Maybe AI. A failure to evaluate gets a grey outline.

Model weights

The ONNX classifier weights are not committed to the repo — they're large (hundreds of MB) and fully reproducible from their upstream sources, so they're git-ignored along with models/. npm run build expects them to exist at:

• models/OpenSynthID/model.onnx — tier 4, SynthID watermark surrogate (from fyxme/opensynthid-detect-0.1)
• models/OpenFake/model.onnx — tier 5, OpenFake SwinV2 real/fake detector (from the ComplexDataLab/OpenFakeDemo Space)

To fetch the upstream weights and convert them to ONNX, run:

./build-models.sh            # fresh clone: download sources + convert to ONNX
./build-models.sh --refresh  # also re-pull upstream weights (after a maintainer update)

The script creates a self-contained Python venv (.venv-convert/, git-ignored), installs the conversion deps, downloads the source checkpoints, and runs convert_model.py for each model. It needs a Python with torch wheels available (prefers python3.13; override with PYTHON=…). Run it once before the first npm run build.

Configuration

The Options page (right-click toolbar icon → Options) has one setting:

• Debug mode — when on, all three contexts (page, service worker, offscreen document) log per-image detection results to their consoles, and AI-flagged overlays show a small monospace line under the label with the rule + per-model score.

Limitations (be aware)

• SynthID detection uses an unvalidated community surrogate. Google's official SynthID Detector is not publicly available. Tier 4 uses fyxme/opensynthid-detect-0.1, a reverse-engineered surrogate (v0.1, author discloses it's unvalidated).

SynthID is a watermarking technology developed by Google DeepMind. SlopGuard is not affiliated with, endorsed by, or connected to Google or DeepMind. Tier 4 does not use Google's SynthID software; it relies on fyxme/opensynthid-detect-0.1, an independent third-party model that attempts to detect the watermark's statistical signature. "SynthID" is used here for identification and descriptive purposes only.

• Grok metadata is stripped. Real Grok images carry no metadata, so tiers 1–3 can't catch them. The tier-5 classifier catches some.

• No background-image detection. Only <img> elements are scanned. Sites that render images via CSS background-image (some Twitter cards) won't be caught.

• C2PA signature validation is not performed — we trust the manifest contents. False-positive risk is low because the strings we look for (e.g. trainedAlgorithmicMedia) are AI-specific.

Development

npm run watch    # esbuild watch mode

Click the Reload button on the extension's card after rebuilding to pick up changes. Page tabs need a refresh too — content scripts don't auto-update on extension reload.

Build outputs

• dist/background.js — service worker (~99KB, includes exifr)
• dist/content.js — content script (page DOM, scan UI)
• dist/offscreen.js — offscreen document (~1.1MB, c2pa-js + onnxruntime-web)
• dist/options.{html,js} — options page
• dist/models/ — ONNX classifier weights (OpenSynthID ~82MB, OpenFake ~350MB), produced by ./build-models.sh — see Model weights
• dist/ort-wasm-* — ONNX runtime WASM
• dist/c2pa.worker.min.js, dist/toolkit_bg.wasm — c2pa-js assets

Adding a visual classifier

1. Convert PyTorch / safetensors → ONNX. For HuggingFace *ForImageClassification models, add a convert_hf_image_classifier(...) call in convert_model.py and run python3.13 convert_model.py — it will read the model's processor for input size + normalization and verify on firefly-sample.jpg.
2. Place the .onnx in models/<YourModel>/
3. Add a copy entry in build.js
4. Add a config to VISUAL_MODELS in src/offscreen.js (input size, mean/std, output interpretation, thresholds)
5. Rebuild

File layout

.
├── manifest.json           # MV3 manifest
├── package.json            # esbuild + dependencies
├── build.js                # esbuild build script
├── build-models.sh         # fetch upstream weights + convert to ONNX (see "Model weights")
├── convert_model.py        # PyTorch/safetensors→ONNX converter (--opensynthid, --openfake, and others)
├── src/
│   ├── background.js       # service worker, fetches bytes, runs tiers 2/3, dispatches to offscreen
│   ├── content.js          # DOM scan, overlays, progress bar, SPA nav handling
│   ├── offscreen.js        # c2pa-js + onnxruntime-web inference (tier 4 SynthID + tier 5 classifier)
│   ├── synthid-preprocess.js  # pure-JS db4 wavelet + FFT + carrier mask for the tier-4 input
│   ├── offscreen.html      # offscreen doc host page
│   ├── options.html, options.js  # extension options
│   └── overlay.css         # label / progress bar / outline styles
├── models/                 # ONNX weights (git-ignored, built by build-models.sh): OpenSynthID + OpenFake copied to dist/
├── templates/              # watermark PNGs (Gemini, Grok) — for the disabled tier 4a
├── test-images/            # 7 AI + 3 real images for broader testing
├── test.html               # local sample page (six AI generators)
├── test-images.html        # broader test page using test-images/
└── *-sample.{jpg,png}      # AI image samples for testing

Licensing

SlopGuard's own source code is licensed GPL-3.0-or-later — see LICENSE.

It bundles third-party models and libraries under their own terms; the full inventory and required attributions are in THIRD-PARTY-LICENSES.md and NOTICE:

• OpenSynthID (tier 4) — Apache-2.0
• OpenFake (tier 5) — CC BY-NC 4.0
• c2pa-js, onnxruntime-web, exifr — MIT

⚠️ The bundled distribution is NonCommercial. OpenFake's CC BY-NC 4.0 term applies to any build that includes the OpenFake model, so the bundle as shipped may not be used commercially.