activity
20192021
most citedDRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing

98 citations · 112 across the 3 of their papers we have counts for

collaborators

7 papers

cs.MM2021

Deep Learning-based Forgery Attack on Document Images

Lin Zhao, Changsheng Chen, Jiwu Huang

With the ongoing popularization of online services, the digital document images have been used in various applications. Meanwhile, there have emerged some deep learning-based text…

cs.MM2021

Domain Generalization for Document Authentication against Practical Recapturing Attacks

Changsheng Chen, Shuzheng Zhang, Fengbo Lan +1

Recapturing attack can be employed as a simple but effective anti-forensic tool for digital document images. Inspired by the document inspection process that compares a questioned…

cs.CV202098 cited

DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing

Rizhao Cai, Haoliang Li, Shiqi Wang +2

Inspired by the philosophy employed by human beings to determine whether a presented face example is genuine or not, i.e., to glance at the example globally first and then carefull…

cs.CR2020

Detection of Information Hiding at Anti-Copying 2D Barcodes

Ning Xie, Ji Hu, Junjie Chen +2

This paper concerns the problem of detecting the use of information hiding at anti-copying 2D barcodes. Prior hidden information detection schemes are either heuristicbased or Mach…

cs.CR2020

Low-Cost Anti-Copying 2D Barcode by Exploiting Channel Noise Characteristics

Ning Xie, Qiqi Zhang, Ji Hu +2

In this paper, for overcoming the drawbacks of the prior approaches, such as low generality, high cost, and high overhead, we propose a Low-Cost Anti-Copying (LCAC) 2D barcode by e…

cs.LG20191 cited

Layer Pruning for Accelerating Very Deep Neural Networks

Weiwei Zhang, Changsheng chen, Xuechun Wu +4

In this paper, we propose an adaptive pruning method. This method can cut off the channel and layer adaptively. The proportion of the layer and the channel to be cut is learned ada…