Blog

Contributed by Dr. Weiqi Luo and co-authors, based on the IEEEXplore® article, “Steganography Embedding Cost Learning With Generative Multi-Adversarial Network”, published in the IEEE Transactions on Information Forensics and Security, 2023.

Introduction

Proposed Method

As shown in Fig. 1, the proposed Steg-GMAN model retains the typical GAN structure while introducing key enhancements to overcome the limitations of related methods.

Fig. 1. The network architecture of the proposed method

This post introduces a novel approach to extending the depth of field (DOF) in lensless cameras using optimized radial coded masks. Our proposed method overcomes the limitations of previous and generic coded mask designs by employing a radial-shape-constrained optimization procedure, resulting in improved optical transfer functions while enabling extended DOF. Through simulations and prototype experiments, we demonstrate that our optimized radial mask achieves superior imaging quality compared to hand-crafted radial patterns and larger DOF than non-radial masks.

An occlusion-aware model for gait video processing uses SMPL-based human mesh models and machine learning to achieve superior recognition in challenging surveillance videos.

Electromagnetic inverse scattering problems (ISPs) are crucial in noninvasive imaging but challenging due to nonlinearity and computational costs. This blog explores machine learning-based ISP solvers with physics-guided loss functions, emphasizing the role of near-field priors and multiple-scattering effects. Numerical experiments highlight the advantages and limitations of these approaches.