Image and video decision
AI Detector - Image Checker
A single competent developer can recreate a usable AI-image-detection web service in about a week using open-source inference or a hosted model; you would give up the vendor’s native iOS app distribution, bundled proprietary models/validation, and the polished UX and App Store subscription plumbing.
View on the App Store↗$2.5/mo
$30/yr
Read off the official pricing page.
$100one-off40 h to build
$50/mo3 h/mo upkeep
On cash alone, building overtakes the subscription at 24 seats.
No open-source build does this yet
Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.
What a replacement has to do
- Upload or take a photo → run an image-forensics detector → compute a 0–100% confidence and colour verdict → show detailed report and allow sharing/history
What it still won’t have
- Proprietary, continuously-updated models and the developer’s model-tuning/validation
- Native iOS App Store distribution and Apple in‑app subscription plumbing
- The developer’s claimed dataset-backed accuracy and research whitepapers backing
- Polished UX, branded shareable reports, and platform-specific optimisations (camera/gallery integrations)
What remains hard
- Product polish and ongoing maintenance
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 24 seats.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal web service: front end in React, backend in Flask (Python), model inference via a hosted inference API or a local PyTorch model. Core features in scope: image upload/camera capture, client-side resize & EXIF stripping, server-side preprocessing, call an image-forensics detector that returns a confidence score, convert score to green/yellow/red verdict, render a detailed report (JSON + shareable PNG), and store recent checks in SQLite with delete/clear operations. Out of scope: App Store signing, in-app purchase integration, paid analytics. Require error handling for uploads, inference failures, and storage; include unit tests for preprocessing, inference integration, and API endpoints; include a README with deployment steps (Dockerfile + docker-compose) and a simple smoke test script.
How we checked
How the score was reached
- Build verdict base78
- 3 cited sources+3
- Price verified on pricing page+3
- Evidence score84
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 3
Every page the run actually retrieved.
- official productAI Detector – Image Checker (App Store)
- official pricingWatch Identifier - AI Scanner (App Store) — pricing source
- official productAI Image Detector: Detectify (App Store) — comparable features
Integrity checks
What held up, and what did not.
