Optimized CNN with Point-Wise Parametric Rectified Linear Unit for Spatial Image Steganalysis

2020 ◽  
pp. 32-42
Author(s):  
Yi-ming Xue ◽  
Wan-li Peng ◽  
Yuzhu Wang ◽  
Juan Wen ◽  
Ping Zhong
Keyword(s):  
Image Steganalysis ◽  
Spatial Image ◽  
Rectified Linear Unit ◽  
Parametric Rectified Linear Unit

DPReLU: Dynamic Parametric Rectified Linear Unit

2020 ◽  
Author(s):  
Kien Mai Ngoc ◽  
Donghun Yang ◽  
Iksoo Shin ◽  
Hoyong Kim ◽  
Myunggwon Hwang
Keyword(s):  
Rectified Linear Unit ◽  
Parametric Rectified Linear Unit

Constructing local information feature for spatial image steganalysis

2016 ◽  
Vol 76 (11) ◽  
pp. 13221-13237 ◽  
Author(s):  
Weiquan Cao ◽  
Qingxiao Guan ◽  
Xianfeng Zhao ◽  
Keren Wang ◽  
Jiesi Han
Keyword(s):  
Local Information ◽  
Image Steganalysis ◽  
Spatial Image

scholarly journals GBRAS-Net: A Convolutional Neural Network Architecture for Spatial Image Steganalysis

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 14340-14350
Author(s):  
Tabares-Soto Reinel ◽  
Arteaga-Arteaga Harold Brayan ◽  
Bravo-Ortiz Mario Alejandro ◽  
Mora-Rubio Alejandro ◽  
Arias-Garzon Daniel ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Image Steganalysis ◽  
Spatial Image

scholarly journals An Intrusion Detection System Based on a Simplified Residual Network

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 356 ◽  
Author(s):  
Yuelei Xiao ◽  
Xing Xiao
Keyword(s):  
Intrusion Detection ◽  
Detection System ◽  
Experimental Results ◽  
Small Scale ◽  
Residual Network ◽  
Detection Systems ◽  
Rectified Linear Unit ◽  
Parametric Rectified Linear Unit ◽  
Low Dimensional ◽  
Residual Block

Residual networks (ResNets) are prone to over-fitting for low-dimensional and small-scale datasets. And the existing intrusion detection systems (IDSs) fail to provide better performance, especially for remote-to-local (R2L) and user-to-root (U2R) attacks. To overcome these problems, a simplified residual network (S-ResNet) is proposed in this paper, which consists of several cascaded, simplified residual blocks. Compared with the original residual block, the simplified residual block deletes a weight layer and two batch normalization (BN) layers, adds a pooling layer, and replaces the rectified linear unit (ReLU) function with the parametric rectified linear unit (PReLU) function. Based on the S-ResNet, a novel IDS was proposed in this paper, which includes a data preprocessing module, a random oversampling module, a S-Resnet layer, a full connection layer and a Softmax layer. The experimental results on the NSL-KDD dataset show that the IDS based on the S-ResNet has a higher accuracy, recall and F1-score than the equal scale ResNet-based IDS, especially for R2L and U2R attacks. And the former has faster convergence velocity than the latter. It proves that the S-ResNet reduces the complexity of the network and effectively prevents over-fitting; thus, it is more suitable for low-dimensional and small-scale datasets than ResNet. Furthermore, the experimental results on the NSL-KDD datasets also show that the IDS based on the S-ResNet achieves better performance in terms of accuracy and recall compared to the existing IDSs, especially for R2L and U2R attacks.

ReST-Net: Diverse Activation Modules and Parallel Subnets-Based CNN for Spatial Image Steganalysis

2018 ◽  
Vol 25 (5) ◽  
pp. 650-654 ◽  
Author(s):  
Bin Li ◽  
Weihang Wei ◽  
Anselmo Ferreira ◽  
Shunquan Tan
Keyword(s):  
Image Steganalysis ◽  
Spatial Image

scholarly journals Sensitivity of deep learning applied to spatial image steganalysis

2021 ◽  
Vol 7 ◽  
pp. e616
Author(s):  
Reinel Tabares-Soto ◽  
Harold Brayan Arteaga-Arteaga ◽  
Alejandro Mora-Rubio ◽  
Mario Alejandro Bravo-Ortíz ◽  
Daniel Arias-Garzón ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Traditional Approach ◽  
Detection Accuracy ◽  
Activation Functions ◽  
Single Model ◽  
Image Steganalysis ◽  
Spatial Image ◽  
Overall Performance ◽  
Report Accuracy

In recent years, the traditional approach to spatial image steganalysis has shifted to deep learning (DL) techniques, which have improved the detection accuracy while combining feature extraction and classification in a single model, usually a convolutional neural network (CNN). The main contribution from researchers in this area is new architectures that further improve detection accuracy. Nevertheless, the preprocessing and partition of the database influence the overall performance of the CNN. This paper presents the results achieved by novel steganalysis networks (Xu-Net, Ye-Net, Yedroudj-Net, SR-Net, Zhu-Net, and GBRAS-Net) using different combinations of image and filter normalization ranges, various database splits, different activation functions for the preprocessing stage, as well as an analysis on the activation maps and how to report accuracy. These results demonstrate how sensible steganalysis systems are to changes in any stage of the process, and how important it is for researchers in this field to register and report their work thoroughly. We also propose a set of recommendations for the design of experiments in steganalysis with DL.

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