scholarly journals Blind Steganalysis for JPEG Images using SVM and SVM-PSO Classifiers

2019 ◽  
Vol 8 (11S) ◽  
pp. 1239-1246 ◽  
Keyword(s):  
Kernel Functions ◽  
Support Vector ◽  
Least Significant Bit ◽  
Jpeg Images ◽  
Lsb Matching ◽  
Universal Steganalysis ◽  
Pixel Value ◽  
Lsb Replacement ◽  
Pixel Value Differencing ◽  
Blind Steganalysis

Blind steganalysis or the universal steganalysis helps to identify hidden information without previous knowledge of the content or the embedding technique. The Support Vector Machine (SVM) and SVM- Particle Swarm Optimization (SVM-PSO) classifiers are adopted for the proposed blind steganalysis. The important features of the JPEG images are extracted using Discrete Cosine Transform (DCT). The kernel functions used for the classifiers in the proposed work are the linear, epanechnikov, multi-quadratic, radial, ANOVA and polynomial. The proposed work uses linear, shuffle, stratified and automatic sampling techniques. The proposed work employs four techniques for image embedding namely, Least Significant Bit (LSB) Matching, LSB replacement, Pixel Value Differencing (PVD) and F5 and applies 25% embedding. The data to the classifier is split as 80:20 for training and testing and 10-fold cross validation is carried out.

scholarly journals Cross-Validation and Blind Feature Analysis of 25 Percent Embedding on JPEG Image Format using SVM

2019 ◽  
Vol 8 (11S) ◽  
pp. 1188-1191 ◽  
Keyword(s):  
Cross Validation ◽  
Support Vector ◽  
Transform Domain ◽  
Jpeg Image ◽  
First Order ◽  
Lsb Matching ◽  
Pixel Value ◽  
Lsb Replacement ◽  
Pixel Value Differencing ◽  
Image Format

This paper provides a result assessment of traditional JPEG picture extraction function steganalysis compared to a cross-validation picture. Four distinct algorithms are used as steganographic systems in the spatial and transform domain. They are LSB Matching, LSB Replacement, Pixel Value Differencing and F5.A 25 percentage of embedding with text embedding information is considered in this paper. The characteristics regarded for evaluation are the First Order, Second Order, Extended DCT characteristics, and Markov characteristics. Support Vector Machine is the classifier used here. In statistical recovery, six distinct kernels and four distinct sampling techniques are used for evaluation.

scholarly journals High Capacity Image Steganography Using Modified LSB Substitution and PVD against Pixel Difference Histogram Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Gandharba Swain
Keyword(s):  
Boundary Problem ◽  
High Capacity ◽  
Experimental Results ◽  
Least Significant Bit ◽  
Rs Analysis ◽  
Distortion Measure ◽  
Pixel Value ◽  
Pixel Value Differencing ◽  
Difference Histogram ◽  
Lsb Substitution

The combination of pixel value differencing (PVD) and least significant bit (LSB) substitution gives higher capacity and lesser distortion. However, there are three issues to be taken into account: (i) fall off boundary problem (FOBP), (ii) pixel difference histogram (PDH) analysis, and (iii) RS analysis. This paper proposes a steganography technique in two variants using combination of modified LSB substitution and PVD by taking care of these three issues. The first variant operates on 2 × 3 pixel blocks and the second technique operates on 3 × 3 pixel blocks. In one of the pixels of a block, embedding is performed using modified LSB substitution. Based on the new value of this pixel, difference values with other neighboring pixels are calculated. Using these differences, PVD approach is applied. The edges in multiple directions are exploited, so PDH analysis cannot detect this steganography. The LSB substitution is performed in only one pixel of the block, so RS analysis also cannot detect this steganography. To address the FOBP, suitable equations are used during embedding procedure. The experimental results such as bit rate and distortion measure are satisfactory.

A Novel Calibration Used for Blind Steganalysis of JPEG Images

2014 ◽  
Vol 971-973 ◽  
pp. 1504-1507
Author(s):  
Ling Wang ◽  
Jing Xin Hong ◽  
Xu Chen ◽  
Qian Chen ◽  
Yi Xiong Zhang
Keyword(s):  
High Accuracy ◽  
Support Vector ◽  
Specific Function ◽  
Calibration Technique ◽  
Stego Image ◽  
Jpeg Images ◽  
Class Separation ◽  
The Difference ◽  
Nonlinear Classifier ◽  
Blind Steganalysis

In this paper, we present a new calibration technique aimed at blind steganalysis for JPEG images, which can magnify the difference between cover images and stego images. The calibration can be considered as a preprocessing of stego image before extracting the features. So the calibrated features are calculated as the difference between a specific function calculated from the original stego image and the same function obtained from calibrated version. Moreover, the calibrated feature was used to train SVM (support vector machine), a nonlinear classifier, which is effective in class separation. For comparison, a database composed of 6690 cover and stego images (generated by using four different embedding schemes) was established. Based on this database, we conducted extensive experiments and drawn a conclusion that the steganalysis based on our novel calibration can detect the stego images with high accuracy.

Statistical Feature–Based Steganalysis for Pixel-Value Differencing Steganography

2021 ◽  
Author(s):  
Wen-Bin Lin ◽  
Tai-Hung Lai ◽  
Ko-Chin Chang
Keyword(s):  
Least Significant Bit ◽  
Stego Image ◽  
Image Steganalysis ◽  
Detection Analysis ◽  
Suitable Parameter ◽  
Feature Based ◽  
Pixel Value ◽  
Pixel Value Differencing ◽  
Difference Histogram ◽  
Advance Knowledge

Abstract The security and embedding capacity of pixel-value differencing (PVD) steganography is superior to that of least significant bit replacement steganography. Several studies have proposed extended PVD steganography methods that use the original concept of PVD steganography. The majority of the studies have verified their security against regular-singular detection analysis or pixel difference histogram attacks. Weighted stego image steganalysis is the state-of-the-art technology for PVD steganography. This study proposed a suitable parameter for the estimator based on different relative embedding ratios and the size of normal embedding blocks. The experimental results revealed that the proposed technology does not require advance knowledge of the original image. In addition, the proposed method is accurate and precise at any embedding ratio. In the future, this method may be utilized to analyze the security of extended PVD steganography.

A Review on LSB Substitution and PVD Based Image Steganography Techniques

2016 ◽  
Vol 2 (3) ◽  
pp. 712 ◽  
Author(s):  
Aditya Kumar Sahu ◽  
Gandharba Swain
Keyword(s):  
Information And Communication Technologies ◽  
Data Communication ◽  
Communication Technologies ◽  
Least Significant Bit ◽  
Hiding Capacity ◽  
Information And Communication ◽  
Distortion Measure ◽  
Pixel Value ◽  
Pixel Value Differencing ◽  
Lsb Substitution

<p>There has been a tremendous growth in Information and Communication technologies during the last decade. Internet has become the dominant media for data communication. But the secrecy of the data is to be taken care. Steganography is a technique for achieving secrecy for the data communicated in Internet. This paper presents a review of the steganography techniques based on least significant bit (LSB) substitution and pixel value differencing (PVD). The various techniques proposed in the literature are discussed and possible comparison is done along with their respective merits. The comparison parameters considered are, (i) hiding capacity, (ii) distortion measure, (iii) security, and (iv) computational complexity.</p>

scholarly journals Information Trouncing Via Slightest Momentous Spot System Procedure

2018 ◽  
Vol 7 (3.27) ◽  
pp. 488
Author(s):  
D Saravanan ◽  
N Sivaprasad ◽  
Dennis Joseph
Keyword(s):  
Visual Quality ◽  
Secret Message ◽  
Pseudorandom Number Generator ◽  
Least Significant Bit ◽  
Pixel Value ◽  
The Difference ◽  
Pixel Value Differencing ◽  
The Relationship ◽  
Embedding Rate

The least-significant-bit based approach is a popular type of stenographic algorithms in the spatial domain. However, we find that in most existing approaches, the choice of embedding positions within a cover audio mainly depends on a pseudorandom number generator without considering the relationship between the audio content itself and the size of the secret message. In this paper, we expand the least significant bit matching revisited audio stegnography and propose an edge adaptive scheme which can select the embedding regions according to the size of secret message and the difference between two consecutive pixels in the cover audio. For lower embedding rates, only sharper edge regions are used while keeping the other smoother regions as they are. When the embedding rate increases, more edge regions can be released adaptively for data hiding by adjusting just a few parameters. New scheme can enhance the security significantly compared with typical least significant bit-based approaches as well as their edge adaptive ones, such as pixel-value-differencing-based approaches, while preserving higher visual quality of stegno audios at the same time.  

scholarly journals Digital Image Steganography Using Eight-Directional PVD against RS Analysis and PDH Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Gandharba Swain
Keyword(s):  
Least Significant Bit ◽  
Hiding Capacity ◽  
Central Pixel ◽  
Rs Analysis ◽  
Pixel Value ◽  
Pixel Value Differencing ◽  
Difference Histogram ◽  
Lsb Substitution

The least significant bit (LSB) substitution techniques are detected by RS analysis and the traditional pixel value differencing (PVD) approaches are detected by pixel difference histogram (PDH) analysis. The PVD steganography can escape from PDH analysis by using the edges in multiple directions. This paper proposes a steganography technique by exploiting the edges in eight directions and also using LSB substitution to resist from both RS analysis and PDH analysis. For every 3×3 pixel block the central pixel is embedded with 3 or 4 bits of data by modified LSB substitution technique. Then this new value of the central pixel is utilized to calculate eight difference values with eight neighboring pixels. These eight difference values are used to hide the data. There are two types with regard to two different range tables. Type 1 uses 3 bit modified LSB substitution and range table 1. Type 2 uses 4 bit modified LSB substitution and range table 2. Type 1 and type 2 are also known as variant 1 and variant 2, respectively. Type 1 possesses higher PSNR and type 2 possesses higher hiding capacity.

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