Detects AI-generated images entirely on-device (WebGPU/WASM). No cloud. No data leaves your browser.
Users
61
+1 in 30 days
Rating
0.0 / 5
unchanged in 30 days
Ratings
0
0 in 30 days
Store rank
#101K
#85K among extensions
Version
0.14.0
updated 31 Aug 2026
Users
monthly, 2 months
Latest, 19 Oct 2026:61 usersMove the mouse over the chart for any month
Ratings count
monthly, 2 months
Latest, 19 Oct 2026:0 ratingsMove the mouse over the chart for any month
The Chrome Web Store rounds user counts down to the leading digit, so the user line moves in steps. The ratings count is exact and is the better signal of real adoption.
About Sieve — Local AI Image Detector
Sieve spots AI-generated images while you browse — and does it entirely on your device.
A compact vision model (fine-tuned from Community Forensics, CVPR 2025) runs locally in your browser via WebGPU or WASM. Every image you scroll past gets a quiet confidence badge; images above your threshold (default 65%) are flagged as AI and can be blurred automatically.
WHY SIEVE
• 100% local: no cloud, no APIs, no accounts, no telemetry — images never leave your browser
• Trained for the modern web: GPT Image, Midjourney v7, Flux, Nano Banana and more, tested against heavy compression and thumbnails
• Knows what's NOT AI: screenshots, cartoon frames, game art, YouTube thumbnails, press photos — trained on real-world categories users actually reported
• Honest uncertainty: borderline images get an amber "unsure" badge instead of a false alarm
• One-click reporting: right-click any wrong verdict to pre-fill a public GitHub issue — reports become regression tests and training data for the next model
• Open source (MIT): github.com/Phineas1500/sieve-ai-image-detector
HOW IT WORKS
On install, Sieve downloads its detection model once (~45 MB) from a pinned, checksum-verified URL. After that it runs fully offline. Scores are computed with deterministic preprocessing and a numerically self-tested GPU path, so results are consistent across machines and browsers.
Winner of the poidh and 0xSero local-AI detection challenge (36k-image benchmark: 90.9% balanced accuracy clean / 87.1% web-degraded / 85.6% heavily compressed, at a fixed 65% threshold).