END$^2$: Robust Dual-Decoder Watermarking Framework Against Non-Differentiable Distortions
Journal:
arXiv
Published Date:
Dec 13, 2024
Abstract
DNN-based watermarking methods have rapidly advanced, with the
``Encoder-Noise Layer-Decoder'' (END) framework being the most widely used. To
ensure end-to-end training, the noise layer in the framework must be
differentiable. However, real-world distortions are often non-differentiable,
leading to challenges in end-to-end training. Existing solutions only treat the
distortion perturbation as additive noise, which does not fully integrate the
effect of distortion in training. To better incorporate non-differentiable
distortions into training, we propose a novel dual-decoder architecture
(END$^2$). Unlike conventional END architecture, our method employs two
structurally identical decoders: the Teacher Decoder, processing pure
watermarked images, and the Student Decoder, handling distortion-perturbed
images. The gradient is backpropagated only through the Teacher Decoder branch
to optimize the encoder thus bypassing the problem of non-differentiability. To
ensure resistance to arbitrary distortions, we enforce alignment of the two
decoders' feature representations by maximizing the cosine similarity between
their intermediate vectors on a hypersphere. Extensive experiments demonstrate
that our scheme outperforms state-of-the-art algorithms under various
non-differentiable distortions. Moreover, even without the differentiability
constraint, our method surpasses baselines with a differentiable noise layer.
Our approach is effective and easily implementable across all END
architectures, enhancing practicality and generalizability.
