An Open Source, MIT licensed implementation of TrustMark watermarking 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 provenance 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 identifier 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.

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
}