scholarly journals An Image Style Transfer Network Using Multilevel Noise Encoding and Its Application in Coverless Steganography

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1152
Author(s):  
Shanqing Zhang ◽  
Shengqi Su ◽  
Li Li ◽  
Qili Zhou ◽  
Jianfeng Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Color Image ◽  
Image Data ◽  
Cover Image ◽  
Stego Image ◽  
Style Transfer ◽  
Secret Information ◽  
Residual Learning ◽  
Short Period

Most of the existing image steganographic approaches embed the secret information imperceptibly into a cover image by slightly modifying its content. However, the modification traces will cause some distortion in the stego-image, especially when embedding color image data that usually contain thousands of bits, which makes successful steganalysis possible. A coverless steganographic approach without any modification for transmitting secret color image is proposed. We propose a diversity image style transfer network using multilevel noise encoding. The network consists of a generator and a loss network. A multilevel noise to encode matching the subsequent convolutional neural network scale is used in the generator. The diversity loss is increased in the loss network so that the network can generate diverse image style transfer results. Residual learning is introduced so that the training speed of network is significantly improved. Experiments show that the network can generate stable results with uniform texture distribution in a short period of time. These image style transfer results can be integrated into our coverless steganography scheme. The performance of our steganography scheme is good in steganographic capacity, anti-steganalysis, security, and robustness.

scholarly journals High-Capacity Image Steganography Algorithm Based on Image Style Transfer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xinliang Bi ◽  
Xiaoyuan Yang ◽  
Chao Wang ◽  
Jia Liu
Keyword(s):  
High Capacity ◽  
Image Features ◽  
Image Steganography ◽  
Cover Image ◽  
Secret Message ◽  
The Internet ◽  
Stego Image ◽  
Style Transfer ◽  
Secret Image ◽  
Secret Information

Steganography is a technique for publicly transmitting secret information through a cover. Most of the existing steganography algorithms are based on modifying the cover image, generating a stego image that is very similar to the cover image but has different pixel values, or establishing a mapping relationship between the stego image and the secret message. Attackers will discover the existence of secret communications from these modifications or differences. In order to solve this problem, we propose a steganography algorithm ISTNet based on image style transfer, which can convert a cover image into another stego image with a completely different style. We have improved the decoder so that the secret image features can be fused with style features in a variety of sizes to improve the accuracy of secret image extraction. The algorithm has the functions of image steganography and image style transfer at the same time, and the images it generates are both stego images and stylized images. Attackers will pay more attention to the style transfer side of the algorithm, but it is difficult to find the steganography side. Experiments show that our algorithm effectively increases the steganography capacity from 0.06 bpp to 8 bpp, and the generated stylized images are not significantly different from the stylized images on the Internet.

scholarly journals Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

scholarly journals Lightweight Convolutional Neural Network Based Intrusion Detection System

2020 ◽  
pp. 808-817
Author(s):  
Vinh Pham ◽  
◽  
Eunil Seo ◽  
Tai-Myoung Chung
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Network Traffic ◽  
Detection System ◽  
Image Data ◽  
Training Data ◽  
Deep Packet Inspection ◽  
Detection Model

Identifying threats contained within encrypted network traffic poses a great challenge to Intrusion Detection Systems (IDS). Because traditional approaches like deep packet inspection could not operate on encrypted network traffic, machine learning-based IDS is a promising solution. However, machine learning-based IDS requires enormous amounts of statistical data based on network traffic flow as input data and also demands high computing power for processing, but is slow in detecting intrusions. We propose a lightweight IDS that transforms raw network traffic into representation images. We begin by inspecting the characteristics of malicious network traffic of the CSE-CIC-IDS2018 dataset. We then adapt methods for effectively representing those characteristics into image data. A Convolutional Neural Network (CNN) based detection model is used to identify malicious traffic underlying within image data. To demonstrate the feasibility of the proposed lightweight IDS, we conduct three simulations on two datasets that contain encrypted traffic with current network attack scenarios. The experiment results show that our proposed IDS is capable of achieving 95% accuracy with a reasonable detection time while requiring relatively small size training data.

Residual Learning Based Convolutional Neural Network for Super Resolution

2019 ◽  
Vol 7 (4) ◽  
pp. 126-129
Author(s):  
Hwei Jen Lin ◽  
◽  
Yoshimasa Tokuyama ◽  
Zi Jun Lin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Residual Learning

scholarly journals HISTOGRAM OF ORIENTED GRADIENT UNTUK DETEKSI EKSPRESI WAJAH MANUSIA

2018 ◽  
Vol 10 (2) ◽  
pp. 81-86
Author(s):  
Marlinda Vasty Overbeek
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Facial Expressions ◽  
Moving Objects ◽  
Image Data ◽  
Gradient Algorithm ◽  
Multi Layer Perceptron ◽  
Histogram Of Oriented Gradient ◽  
Perceptron Algorithm ◽  
Feature Extracting

This research focuses on the detection of human facial expressions using the Histogram of Oriented Gradient algorithm. Whereas for the classification algorithm, Convolutional Neural Network is used. Image data used in the form of seven different expressions of humans with the extraction of 48x48 pixels. The use of Histogram of Oriented Gradient as a feature extracting algorithm, because Histogram of Oriented Gradient is good to be used in detecting moving objects. Whereas Convolutional Neural Network is used because it is an improvement of the Multi Layer Perceptron algorithm. Of the three epoches done, it produced the best accuracy of 77% re-introduction of human facial expressions. These results are quite convincing because it only uses three epochs.

scholarly journals A Normalized Convolutional Neural Network for Guided Sparse Depth Upsampling

2018 ◽  
Author(s):  
Jiashen Hua ◽  
Xiaojin Gong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Color Image ◽  
State Of The Art ◽  
Depth Map ◽  
Sparse Data ◽  
Optimal Choice ◽  
Deep Convolutional Neural Networks ◽  
Normalized Convolution ◽  
Indoor And Outdoor

Guided sparse depth upsampling aims to upsample an irregularly sampled sparse depth map when an aligned high-resolution color image is given as guidance. When deep convolutional neural networks (CNNs) become the optimal choice to many applications nowadays, how to deal with irregular and sparse data still remains a non-trivial problem. Inspired by the classical normalized convolution operation, this work proposes a normalized convolutional layer (NCL) implemented in CNNs. Sparse data are therefore explicitly considered in CNNs by the separation of both data and filters into a signal part and a certainty part. Based upon NCLs, we design a normalized convolutional neural network (NCNN) to perform guided sparse depth upsampling. Experiments on both indoor and outdoor datasets show that the proposed NCNN models achieve state-of-the-art upsampling performance. Moreover, the models using NCLs gain a great generalization ability to different sparsity levels.

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