An inexact column-and-constraint generation method to solve two-stage robust optimization problems

We propose a new inexact column-and-constraint generation (i-C&CG) method to solve two-stage robust optimization problems. The method allows solutions to the master problems to be inexact, which is desirable when solving large-scale and/or challenging problems. It is equipped with a backtracking routine that controls the trade-off between bound improvement and inexactness. Importantly, this routine allows us to derive theoretical finite convergence guarantees for our i-C&CG method. Numerical experiments demonstrate computational advantages of our i-C&CG method over state-of-the-art column-and-constraint generation methods.     

Screen-shooting resistant image watermarking based on lightweight neural network in frequency domain

Currently, digital mobile devices, especially smartphones, can be used to acquire information conveniently through photograph taking. To protect information security in this case, we propose an efficient screen-shooting resistant watermarking scheme via deep neural network (DNN) in the frequency domain to achieve additional information embedding and source tracing. Specifically, we enhance the imperceptibility of watermarked images and the robustness against various attacks in real scene by computing the residual watermark message and encoding it with the original image using a lightweight neural network in the DCT domain. In addition, a noise layer is designed to simulate the photometric and radiometric effects of screen-shooting transfer. During the training process, the enhancing network is used to highlight the coding features of distorted images and improve the accuracy of extracted watermark message. Experimental results demonstrate that our scheme not only effectively ensures the balance between the imperceptibility of watermark embedding and the robustness of watermark extraction, but also significantly improves computational efficiency compared with some state-of-the-art schemes.