scholarly journals Auto Key Generation and Minimal Image Distortion Mechanism for Image Steganography

2018 ◽  
Vol 179 (17) ◽  
pp. 36-40
Author(s):  
Yuvraj Hembade ◽  
Nikita Bhanose ◽  
Grishma Kulkarni ◽  
Utkarsha Memane ◽  
Jayashree Patil
Keyword(s):  
Image Distortion ◽  
Image Steganography ◽  
Key Generation ◽  
Distortion Mechanism

RGB Frequency Based Image Steganography

2020 ◽  
pp. 549-553
Author(s):  
Himanshu Kumar ◽  
Nitesh Kumar
Keyword(s):  
Image Steganography ◽  
Minimum Number

In this paper, we introduced a new RGB technique for image steganography. In this technique we introduced the idea of storing a different number of bits per channel (R, G or B) of a pixel based on the frequency of color values of pixel. The higher color frequency retains the maximum number of bits and lower color frequency stores the minimum number of bits.

Improved LSB method for Image Steganography using H´enon Chaotic Map

2014 ◽  
Vol 2014 (1) ◽  
pp. 34-42 ◽  
Author(s):  
N. S. Raghava ◽  
◽  
Ashish Kumar ◽  
Aishwarya Deep ◽  
Abhilasha Chahal ◽  
...  
Keyword(s):  
Chaotic Map ◽  
Image Steganography

An Efficient Key Generation of ZHFE Public Key Cryptosystem

2018 ◽  
Vol E101.A (1) ◽  
pp. 29-38
Author(s):  
Yasuhiko IKEMATSU ◽  
Dung Hoang DUONG ◽  
Albrecht PETZOLDT ◽  
Tsuyoshi TAKAGI
Keyword(s):  
Public Key ◽  
Public Key Cryptosystem ◽  
Key Generation

No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics

2020 ◽  
Vol 64 (1) ◽  
pp. 10505-1-10505-16
Author(s):  
Yin Zhang ◽  
Xuehan Bai ◽  
Junhua Yan ◽  
Yongqi Xiao ◽  
C. R. Chatwin ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Image Distortion ◽  
Image Feature ◽  
Reference Image ◽  
Gradient Magnitude ◽  
Distortion Type

Abstract A new blind image quality assessment method called No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics is proposed, which is aimed at solving the problem that the existing no-reference image quality assessment methods cannot determine the type of image distortion and that the quality evaluation has poor robustness for different types of distortion. In this article, an 18-dimensional image feature vector is constructed from gradient magnitude features, relative gradient orientation features, and relative gradient magnitude features over two scales and three orders on the basis of the relationship between multi-order gradient statistics and the type and degree of image distortion. The feature matrix and distortion types of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion type; the feature matrix and subjective scores of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion degree. A series of comparative experiments were carried out using Laboratory of Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality, Tampere Image, and Optics Remote Sensing Image databases. Experimental results show that the proposed method has high distortion type judgment accuracy and that the quality score shows good subjective consistency and robustness for all types of distortion. The performance of the proposed method is not constricted to a particular database, and the proposed method has high operational efficiency.

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