Robust Fourier Watermarking for Print-Cam Process using Convolutional Neural Networks

Nowadays, with the development of technology and the popularity of smartphones, dealing with captured documents has become an inevitable necessity. The print-cam process produces serious attacks during capturing images, especially perspective distortions. In this paper, we have improved the Fourier watermarking method proposed by Gourrame et al. [1] for Print-Cam attacks which is based on a specific correction preprocess. This correction consists of a frame-based perspective rectification for the captured images. Effectively, Instead of applying a classical Hough detecting-based technique to restore the geometric distortions, we have implemented a new deep learning neural architecture that enables us to segment the image more accurately. According to the tests carried out, this architecture allowed us to improve the robustness of the global watermarking method.