Blind Deep-Learning-Based Image Watermarking Robust Against Geometric
  Transformations
Hannes Mareen Lucas Antchougov Glenn Van Wallendael Peter Lambert
Digital watermarking enables protection against copyright infringement of
images. Although existing methods embed watermarks imperceptibly and
demonstrate robustness against attacks, they typically lack resilience against
geometric transformations. Therefore, this paper proposes a new watermarking
method that is robust against geometric attacks. The proposed method is based
on the existing HiDDeN architecture that uses deep learning for watermark
encoding and decoding. We add new noise layers to this architecture, namely for
a differentiable JPEG estimation, rotation, rescaling, translation, shearing
and mirroring. We demonstrate that our method outperforms the state of the art
when it comes to geometric robustness. In conclusion, the proposed method can
be used to protect images when viewed on consumers' devices.