SECURE IMAGE HIDING SCHEME BASED UPON VECTOR QUANTIZATION

2004 ◽  
Vol 18 (06) ◽  
pp. 1111-1130 ◽  
Author(s):  
YU-CHEN HU ◽  
MIN-HUI LIN
Keyword(s):  
Vector Quantization ◽  
Experimental Results ◽  
Cover Image ◽  
Quantization Scheme ◽  
Gray Level ◽  
Host Image ◽  
Image Hiding ◽  
Gray Level Image ◽  
Multiple Secret Images ◽  
Average Image

In this paper, a novel gray-level image-hiding scheme is proposed. The goal of this scheme is to hide multiple important gray-level images into another meaningful gray-level image. The secret images to be protected are first compressed using the vector quantization scheme. Then, the DES cryptosystem is conducted on the VQ indices and related parameters to generate the encrypted message. Finally, the encrypted message is embedded into the rightmost two bits of each pixel in the cover image. According to the experimental results, average image qualities of 44.320 dB and 30.885 dB are achieved for the embedded images and the retrieved secret images, respectively. In other words, multiple secret images can be effectively hidden into one host image of the same size. In addition, the proposed scheme strengthens the protection of the secret images by conducting the DES cryptosystem on the related parameters and the VQ indices of the compressed secret images. Therefore, the proposed scheme provides a secure approach to embed multiple important images into another meaningful image of the same size.

scholarly journals Gray Level Image Threshold Using Neutrosophic Shannon Entropy

2019 ◽  
Author(s):  
Vasile Patrascu
Keyword(s):  
Shannon Entropy ◽  
Experimental Results ◽  
New Method ◽  
Gray Level ◽  
Segmentation Method ◽  
Separation Threshold ◽  
Multiple Thresholds ◽  
Gray Level Image ◽  
Information Components ◽  
Grayscale Images

This article presents a new method of segmenting grayscale images by minimizing Shannon's neutrosophic entropy. For the proposed segmentation method, the neutrosophic information components, i.e., the degree of truth, the degree of neutrality and the degree of falsity are defined taking into account the belonging to the segmented regions and at the same time to the separation threshold area. The principle of the method is simple and easy to understand and can lead to multiple thresholds. The efficacy of the method is illustrated using some test gray level images. The experimental results show that the proposed method has good performance for segmentation with optimal gray level thresholds.

scholarly journals Gray Level Image Hiding by Using Mackey-Glass Series

2013 ◽  
Vol 5 (2) ◽  
pp. 553-557
Author(s):  
Rasul Enayatifar ◽  
Abdul Hanan Abdullah ◽  
Abdulrahman A. Mirza ◽  
Maqsood Mahmud
Keyword(s):  
Gray Level ◽  
Image Hiding ◽  
Gray Level Image ◽  
Glass Series

scholarly journals A Location Map Reduced Reversible Data Hiding Method Using Prediction Error Modification

2012 ◽  
Vol 6-7 ◽  
pp. 428-433
Author(s):  
Yan Wei Li ◽  
Mei Chen Wu ◽  
Tung Shou Chen ◽  
Wien Hong
Keyword(s):  
Image Quality ◽  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
Experimental Results ◽  
Cover Image ◽  
Prediction Errors ◽  
Secret Data ◽  
Location Map ◽  
Comparable Image Quality

We propose a reversible data hiding technique to improve Hong and Chen’s (2010) method. Hong and Chen divide the cover image into pixel group, and use reference pixels to predict other pixel values. Data are then embedded by modifying the prediction errors. However, when solving the overflow and underflow problems, they employ a location map to record the position of saturated pixels, and these pixels will not be used to carry data. In their method, if the image has a plenty of saturated pixels, the payload is decreased significantly because a lot of saturated pixels will not joint the embedment. We improve Hong and Chen’s method such that the saturated pixels can be used to carry data. The positions of these saturated pixels are then recorded in a location map, and the location map is embedded together with the secret data. The experimental results illustrate that the proposed method has better payload, will providing a comparable image quality.

scholarly journals Double stages of feature extarction-based GFPMI for colored finger vein identification

2020 ◽  
Vol 18 (2) ◽  
pp. 927
Author(s):  
Dawlat Mustafa Sulaiman ◽  
Adnan Mohsin Abdulazeez ◽  
Habibollah Haron
Keyword(s):  
Feature Extraction ◽  
Texture Features ◽  
Biometric Identification ◽  
Gray Level ◽  
Image Detection ◽  
Structure Information ◽  
Finger Vein ◽  
Gray Level Image ◽  
Vein Recognition ◽  
Finger Vein Recognition

Today, finger vein recognition has a lot of attention as a promising approach of biometric identification framework and still does not meet the challenges of the researchers on this filed. To solve this problem, we propose s double stage of feature extraction schemes based localized finger fine image detection. We propose Globalized Features Pattern Map Indication (GFPMI) to extract the globalized finger vein line features basede on using two generated vein image datasets: original gray level color, globalized finger vein line feature, original localized gray level image, and the colored localized finger vein images. Then, two kinds of features (gray scale and texture features) are extracted, which tell the structure information of the whole finger vein pattern in the whole dataset. The recurrent based residual neural network (RNN) is used to identify the finger vein images. The experimental show that the localized colored finger vein images based globalized feature extraction has achieved the higher accuracy (93.49%) while the original image dataset achieved less accuracy by (69.86%).

scholarly journals Pengenalan Ras Berdasarkan Hidung Dan Mulut Menggunakan Gray Level Co-Occurrence Matrix

2021 ◽  
Vol 8 (4) ◽  
pp. 729
Author(s):  
Ema Rachmawati ◽  
Nur Azizah Agustina ◽  
Febryanti Sthevanie
Keyword(s):  
Random Forest ◽  
System Performance ◽  
Experimental Results ◽  
Gray Level ◽  
The Face ◽  
Combined Features ◽  
Occurrence Matrix ◽  
Large Groups ◽  
Performance Of Race

<p class="Abstract">Ras dapat digunakan untuk mengkategorikan manusia dalam populasi atau kelompok besar. Oleh karena itu, pengenalan ras dapat berguna untuk mempermudah dalam mengidentifikasi seseorang dan membantu dalam mempersempit lingkup pencarian. Penggunaan wajah sebagai dasar pengenalan ras mengarahkan penelitian pada identifikasi penggunaan bagian wajah yang berpengaruh signifikan terhadap kinerja pengenalan ras. Pada penelitian ini bagian wajah berupa hidung dan mulut diidentifikasi untuk digunakan sebagai dasar pengenalan ras Mongoloid, Kaukasoid, dan Negroid. Ciri <em>Gray Level Co-occurrence Matrix</em> (GLCM) diekstrak dari bagian hidung dan mulut untuk selanjutnya diklasifikasi menggunakan Random Forest. Hasil eksperimen menunjukkan bahwa penggunaan ciri gabungan dari hidung dan mulut mampu menghasilkan kinerja sistem yang paling baik jika dibandingkan penggunaan hidung atau mulut saja.</p><p class="Abstract"> </p><p class="Abstract"><strong><em>Abst</em></strong><strong><em>r</em></strong><strong><em>act</em></strong></p><p align="center"><em>Race can be used to categorize humans in populations or large groups. Therefore, racial recognition can be useful to make it easier to identify a person and help narrow the scope of the search. The use of faces as a basis for race recognition directs research on identifying the use of facial parts that significantly influence the performance of race recognition. In this study, the face parts of the nose and mouth were identified to be used as a basis for the recognition of the Mongoloid, Caucasoid, and Negroid races. The Gray Level Co-occurrence Matrix (GLCM) feature is extracted from the nose and mouth to be classified using Random Forest. The experimental results show that the use of combined features of the nose and mouth is able to produce the best system performance compared to the use of the nose or mouth only.</em></p><p class="Abstract"> </p>

Automatic Segmentation of Pores in Weld Images Based on Transition Region Extraction

2012 ◽  
Vol 217-219 ◽  
pp. 1964-1967
Author(s):  
Tong Tong ◽  
Yan Cai ◽  
Da Wei Sun ◽  
Peng Liu
Keyword(s):  
Transition Region ◽  
Automatic Segmentation ◽  
Experimental Results ◽  
Gray Level ◽  
Segmentation Method ◽  
Complex Environment ◽  
Weld Defects ◽  
Contrast Enhanced ◽  
Region Extraction ◽  
Low Pass

In allusion to the complex images of weld defects, weak contrast between the target and the background, a new segmentation method based on gray level difference transition region extraction is proposed. The paper analyzes the characteristic of weld defects, and then low-pass filtering and contrast enhanced are used to enhance the clarity. Finally, we extract the transition region and confirm a threshold for defects segmentation. The experimental results show that the method can extract the transition region more accurate, and segment the image much better in complex environment.

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