An Open Source, MIT licensed implementation of TrustMark watemarking for the Content Authenticity Initiative (CAI) as described in:
TrustMark - Universal Watermarking for Arbitrary Resolution Images
https://arxiv.org/abs/2311.18297
Tu Bui 1, Shruti Agarwal 2 , John Collomosse 1,2
1 DECaDE Centre for the Decentralized Digital Economy, University of Surrey, UK.
2 Adobe Research, San Jose CA.
This repo contains a Python (3.8.5 or higher) implementation of TrustMark for encoding, decoding and removing image watermarks.
Within the python folder run pip install . \
The python/test.py script provides examples of watermarking images (a JPEG and a transparent PNG image are provided as examples). To test the installation the following code snippet in Python shows typical usage:
from trustmark import TrustMark
from PIL import Image
# init
tm=TrustMark(verbose=True, model_type='Q')
# encoding example
cover = Image.open('ufo_240.jpg').convert('RGB')
tm.encode(cover, 'mysecret').save('ufo_240_Q.png')
# decoding example
cover = Image.open('ufo_240_Q.png').convert('RGB')
wm_secret, wm_present, wm_schema = tm.decode(cover)
if wm_present:
print(f'Extracted secret: {wm_secret}')
else:
print('No watermark detected')
# removal example
stego = Image.open('ufo_240_Q.png').convert('RGB')
im_recover = tm.remove_watermark(stego)
im_recover.save('recovered.png')Models are now fetched on first use, due to the number of variants and size of models they are not packaged as binary any more.
We recommend use of the Q (quality) model variant. Other variants are packaged for historial / academic paper reproduction purposes but exhibit a lower PSNR.
The following clean install should work for getting up and running on GPU using the python implementation in this repo.
conda create --name trustmark python=3.10
conda activate trustmark
conda install pytorch cudatoolkit=12.8 -c pytorch -c conda-forge
pip install torch==2.1.2 torchvision==0.16.2 -f https://download.pytorch.org/whl/torch_stable.html
pip install .
Packaged TrustMark models/code are trained to encode a payload of 100 bits.
To promote interoperability we recommend following the data schema implemented in python/datalayer. This affords for a user selectable level of error correction over the raw 100 bits of payload.
Encoding.BCH_5- Protected payload of 61 bits (+ 35 ECC bits) - allows for 5 bit flips.Encoding.BCH_4- Protected payload of 68 bits (+ 28 ECC bits) - allows for 4 bit flips.Encoding.BCH_3- Protected payload of 75 bits (+ 21 ECC bits) - allows for 3 bit flips.Encoding.BCH_SUPER- Protected payload of 40 bits (+ 56 ECC bits) - allows for 8 bit flips.
For example instantiate the encoder as:
tm=TrustMark(verbose=True, model_type='Q', encoding_type=TrustMark.Encoding.BCH_5)
The decoder will automatically detect the data schema in a given TrustMark, allowing for user selectable level of robustness.
The raw 100 bits break down into D+E+V=100 bits, where D is the protect payload (e.g. 61) and E are the error correction parity bits (e.g. 35) and V are the version bits (always 4). The version bits comprise 2 reserved (unused) bits, and 2 bits encoding an integer in range 0-3 which indicate the trustmark data schema in use (see python/datalayer.py for the numeric codes).
TrustMark may be used to directly encode a 'soft binding' identifier, which may be used to look up provenace metadata (manifest). This identifier should be encoded via one of the Encoding types BCH_n described above.
TrustMark may alternatively be used to indicate the presence of another watermarking technology that carries an identifier. In this mode the encoding should be Encoding.BCH_SUPER and the payload contain an integer identifer that describes the co-present watermarking technology. This value should be taken from the C2PA Soft Binding Algorithm List.
An example of direct encoding for C2PA is included in c2pa/c2pa_watermark_example.py including the C2PA manifest that should be used to describe the watermark insertion.
TrustMark will detect if CUDA is available and use GPU if so, else default to CPU encode/decode.
The following clean install should work for getting up and running on GPU using the python implementation in this repo.
conda create --name trustmark python=3.10
conda activate trustmark
conda install pytorch cudatoolkit=12.8 -c pytorch -c conda-forge
pip install torch==2.1.2 torchvision==0.16.2 -f https://download.pytorch.org/whl/torch_stable.html
pip install .
If you find this work useful we request you please cite the repo and/or TrustMark paper as follows.
@article{trustmark,
title={Trustmark: Universal Watermarking for Arbitrary Resolution Images},
author={Bui, Tu and Agarwal, Shruti and Collomosse, John},
journal = {ArXiv e-prints},
archivePrefix = "arXiv",
eprint = {2311.18297},
year = 2023,
month = nov
}