Coverless image steganography framework using distance local binary pattern and convolutional neural network

2020 ◽  
Author(s):  
Luis Alberto Sandoval-Bravo ◽  
Volodymyr I. Ponomaryov ◽  
Rogelio Reyes-Reyes ◽  
Clara Cruz-Ramos
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Local Binary Pattern ◽  
Image Steganography

scholarly journals KLASIFIKASI TIPE SEL NORMAL/ABNORMAL BERDASARKAN CITRA PAP-SMEAR MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

2019 ◽  
Vol 6 (1) ◽  
pp. 35-41
Author(s):  
Muhamad Fathurahman ◽  
Rachmadhani Ajeng Nurmufti ◽  
Elan Suherlan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Local Binary Pattern ◽  
Pap Smear ◽  
Cell Types ◽  
Cancer Type ◽  
Manual Analysis ◽  
Testing Data ◽  
Occurrence Matrix

The classification of cell types plays an essential role in monitoring the growth of cancer cells. One of the methods to determine the cancer type is to analyze the pap-smear images manually. Nevertheless, the manual analysis of pap-smear images by the expert has several limitations, such as time-consuming and prone to misdiagnosis. For reducing the risks, it requires the automatic classification of cell types based on pap-smear images. This study utilizes the convolutional neural network (CNN) architectures to automatically classify the cell type into two-class categories (normal/abnormal) based on three features. These features, such as the local binary pattern, gray level co-occurrence matrix, and shape features, are extracted from pap-smear images. This study shows the performance of CNN achieved the maximum accuracy of 99.98%, 100.0%, 99.78% in training, validation, and testing data. Our approach also outperforms the performance of the baseline methods.    Keywords : CNN, Classification, Cell, Neural Network, Pap-smear

scholarly journals Chicken-Moth Search Optimization-based Deep Convolutional Neural Network for Image Steganography

2020 ◽  
Vol 21 (2) ◽  
pp. 217-232
Author(s):  
Reshma V K ◽  
Vinod Kumar R S ◽  
Shahi D ◽  
Shyjith M B
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Deep Convolutional Neural Network ◽  
Image Steganography ◽  
Secret Message ◽  
Search Optimization ◽  
Tetrolet Transform

Image steganography is considered as one of the promising and popular techniques utilized to maintain the confidentiality of the secret message that is embedded in an image. Even though there are various techniques available in the previous works, an approach providing better results is still the challenge. Therefore, an effective pixel prediction based on image stegonography is developed, which employs error dependent Deep Convolutional Neural Network (DCNN) classifier for pixel identification. Here, the best pixels are identified from the medical image based on DCNN classifier using pixel features, like texture, wavelet energy, Gabor, scattering features, and so on. The DCNN is optimally trained using Chicken-Moth search optimization (CMSO). The CMSO is designed by integrating Chicken Swarm Optimization (CSO) and Moth Search Optimization (MSO) algorithm based on limited error. Subsequently, the Tetrolet transform is fed to the predicted pixel for the embedding process. At last, the inverse tetrolet transform is used for extracting the secret message from an embedded image. The experimentation is carried out using BRATS dataset, and the performance of image stegonography based on CMSO-DCNN+tetrolet is evaluated based on correlation coefficient, Structural Similarity Index, and Peak Signal to Noise Ratio, which attained 0.85, 46.981dB, and 0.6388, for the image with noise.  

scholarly journals Test Accuracy Improvement in Face Recognition Using Convolutional Neural Networks

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2447-2451
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Face Recognition ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Scale Invariance ◽  
Local Binary Pattern ◽  
Test Accuracy ◽  
Accuracy Improvement ◽  
The Face

Now-a-days face recognition plays a major role in identifying face of the specific person. There are different face recognition algorithms such as Eigenfaces algorithm, Local binary pattern histograms, Fisherfaces algorithm. All these algorithms face the problem of subject independence as well as translation, rotation, and scale invariance in the recognition of facial expression. In this study, the face recognition using neural network and convolutional neural network (CNN) techniques were utilized and implemented with the help of Python software 3.6.6. It is noticed that the test accuracy is improved against translation, rotation, and scale invariance in face recognition using CNN.

Sign in / Sign up

Export Citation Format

Share Document