ClearMark: Intuitive and Robust Model Watermarking via Transposed Model
  Training
Torsten Krauß Jasper Stang Alexandra Dmitrienko
Due to costly efforts during data acquisition and model training, Deep Neural
Networks (DNNs) belong to the intellectual property of the model creator.
Hence, unauthorized use, theft, or modification may lead to legal
repercussions. Existing DNN watermarking methods for ownership proof are often
non-intuitive, embed human-invisible marks, require trust in algorithmic
assessment that lacks human-understandable attributes, and rely on rigid
thresholds, making it susceptible to failure in cases of partial watermark
erasure.
  This paper introduces ClearMark, the first DNN watermarking method designed
for intuitive human assessment. ClearMark embeds visible watermarks, enabling
human decision-making without rigid value thresholds while allowing
technology-assisted evaluations. ClearMark defines a transposed model
architecture allowing to use of the model in a backward fashion to interwove
the watermark with the main task within all model parameters. Compared to
existing watermarking methods, ClearMark produces visual watermarks that are
easy for humans to understand without requiring complex verification algorithms
or strict thresholds. The watermark is embedded within all model parameters and
entangled with the main task, exhibiting superior robustness. It shows an
8,544-bit watermark capacity comparable to the strongest existing work.
Crucially, ClearMark's effectiveness is model and dataset-agnostic, and
resilient against adversarial model manipulations, as demonstrated in a
comprehensive study performed with four datasets and seven architectures.