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alderstonelabs/
nonescape-v0

Name: nonescape-v0
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Nonescape AI-generated image classifier

This model predicts whether an input image was created by an artificial‑image generator (e.g. Stable Diffusion, Midjourney, GAN, deepfake pipeline) or captured with a conventional camera. It ships with Python and JavaScript/TypeScript SDKs and open‑weights in both Safetensors and ONNX formats.

Model Details

Model Description

Nonescape is a binary image‑classification model trained to distinguish synthetic (AI‑generated) from authentic (photographic/hand‑crafted) imagery.

Developed by: Aedilic Inc.
Model type: Image Classifier
License: Apache 2.0

Model Sources

Repository: https://github.com/aediliclabs/nonescape
Demo: https://www.nonescape.com

Uses

Use‑case	Description	End Users
Content moderation	Flag AI artwork uploads on social platforms or stock‑media sites	Trust & Safety teams
Media forensics	Assist journalists and OSINT researchers verifying image provenance	Investigative reporters, fact‑checkers
Legal evidence triage	Early filtering of manipulated exhibits	Attorneys, e‑discovery vendors

How to Get Started with the Model

Python library

python

from nonescape import NonescapeClassifier

# Load model
classifier = NonescapeClassifier("nonescape-v0.safetensors")

# Single image
result = classifier.predict("image.jpg")
print(f"AI Generated: {result.is_synthetic}, Confidence: {result.confidence}")

# Multiple images
results = classifier.predict(["img1.jpg", "img2.jpg"])

Javascript/NodeJS library

javascript

import { LocalClassifier } from '@aedilic/nonescape';

const classifier = new LocalClassifier({
  onProgress: (progress) => console.log(`Loading: ${Math.round(progress.current / progress.total * 100)}%`)
});

await classifier.initialize();

// Single image
const result = await classifier.predict(imageFile);
console.log(`AI Generated: ${result.isSynthetic}, Confidence: ${result.confidence}`);

// Multiple images
const results = await classifier.predict([image1, image2, image3]);

classifier.dispose();

Recommendations

Use a high threshold (e.g. 0.9) for tasks like filtering images, that require a low false-positive rate
Use the default threshold of 0.5 for tasks like content moderation, that require a low false-negative rate but can live with some false positives

Training Details

The model was trained on 1M+ images scraped from across the internet.

Evaluation

The model was evaluated on the Adversarial AI-Art dataset to make it comparable with other SOTA models. It achieves the best overall accuracy, outperforming open-source and commercial models.

The GitHub repository contains all code to replicate the model evaluation.

alderstonelabs/nonescape-v0