README.md

---
license: mit
tags:
- resnet
- stable-diffusion
- stable-diffusion-diffusers
---

# BZH watermark detector (demo)

You can use this classifier to detect watermarks generated with our [SDXL-turbo watermarking demo](https://huggingface.co/spaces/imatag/stable-signature-bzh).

## Usage

```py
from transformers import AutoModel, BlipImageProcessor
from PIL import Image
import sys
import torch

image_processor = BlipImageProcessor.from_pretrained("imatag/stable-signature-bzh-detector-resnet18")
commit_hash = "584a7bc01dc0f02e53bf8b8b295717ed09ed7294"
model = AutoModel.from_pretrained("imatag/stable-signature-bzh-detector-resnet18", trust_remote_code=True, revision=commit_hash)

img = Image.open(sys.argv[1]).convert("RGB")
inputs = image_processor(img, return_tensors="pt")
with torch.no_grad():
    p = torch.sigmoid(model(**inputs).logits).item()

print(f"approximate p-value: {p}")
```

## Purpose

This model is an approximate version of [IMATAG](https://www.imatag.com/)'s BZH decoder for the watermark embedded in our [SDXL-turbo watermarking demo](https://huggingface.co/spaces/imatag/stable-signature-bzh).
It works on this watermark only and cannot be used to decode other watermarks.
It will produce an approximate p-value measuring the risk of mistakenly detecting a watermark on a benign (non-watermarked) image. For an exact p-value and improved robustness, please use the [API](https://huggingface.co/spaces/imatag/stable-signature-bzh/resolve/main/detect_api.py) instead.

For more details on this watermarking technique, check out our [announcement](https://www.imatag.com/blog/unlocking-the-future-of-content-authentication-imatags-breakthrough-in-ai-generated-image-watermarking) and our lab's [blog post](https://imatag-lab.medium.com/stable-signature-meets-bzh-53ad0ba13691).

For watermarked models with a different key, support for payload, other perceptual compromises, robustness to other attacks, or faster detection, please [contact IMATAG](https://pages.imatag.com/contact-us-imatag).