A New Steganographic Algorithm Based on Multi Directional PVD and Modified LSB

Darabkh, Khalid A.

doi:10.5755/j01.itc.46.1.15253

Cited by 54 publications

(25 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…x stands for the standard deviation of pixel values of original image, 2 y stands for the standard deviation of pixel values of stego image, and xy is the covariance between original image and stego image. Equations (17), (18), (19), (20), and (21) are used to calculate these values. Table 3 notes the results of seven-way PVD [18] which is an extension of Wu and Tsai's [1] technique.…”

Section: Resultsmentioning

confidence: 99%

“…To prevent the detection from pixel difference histogram (PDH) analysis, multidirectional edges have been exploited in [18]. Darabkh et al [19] also proposed PVD steganography using eight-directional PVD which is an extension from Wu and Tsai's original PVD. A PVD steganography technique which is an extension of Wu and Tsai's PVD approach should be verified by PDH analysis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Swain

2018

Advances in Multimedia

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Swain

2018

Advances in Multimedia

View full text Add to dashboard Cite

show abstract

“…Early image steganographic methods include the least significant bit (LSB) substitution method [1], which replaces the least significant bits of image pixels by secret messages, and the pixel value differencing (PVD) methods [2][3][4] that determine the amount of secret messages to be embedded in proportion to the difference between adjacent pixels. These early image steganographic methods sequentially embed secret messages into all pixels of an image, although they have been recently extended to embed messages in randomly selected pixels using pseudo-random generators for secure message hiding [5,6].…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Ternary Classification for Image Steganalysis

Kang

Park

2019

Electronics

View full text Add to dashboard Cite

This study proposes a convolutional neural network (CNN)-based steganalytic method that allows ternary classification to simultaneously identify WOW and UNIWARD, which are representative adaptive image steganographic algorithms. WOW and UNIWARD have very similar message embedding methods in terms of measuring and minimizing the degree of distortion of images caused by message embedding. This similarity between WOW and UNIWARD makes it difficult to distinguish between both algorithms even in a CNN-based classifier. Our experiments particularly show that WOW and UNIWARD cannot be distinguished by simply combining binary CNN-based classifiers learned to separately identify both algorithms. Therefore, to identify and classify WOW and UNIWARD, WOW and UNIWARD must be learned at the same time using a single CNN-based classifier designed for ternary classification. This study proposes a method for ternary classification that learns and classifies cover, WOW stego, and UNIWARD stego images using a single CNN-based classifier. A CNN structure and a preprocessing filter are also proposed to effectively classify/identify WOW and UNIWARD. Experiments using BOSSBase 1.01 database images confirmed that the proposed method could make a ternary classification with an accuracy of approximately 72%.

show abstract

“…Computer applications have become an essential part of the educational process in almost every field . Science, mathematics, engineering, computer networking, medical specialties, and even literature are some of the fields which use computer programs in their courses to apply the skills taught theoretically in the class room in a practical way and to create a simulated environment which make the learning process more interactive, realistic, and even efficient compared to the old traditional methods .…”

Section: Introductionmentioning

confidence: 99%

An efficient speech recognition system for arm‐disabled students based on isolated words

Darabkh

Haddad

Sweidan

et al. 2017

Comp Applic In Engineering

Self Cite

View full text Add to dashboard Cite

Over the previous decades, a need has emerged to empower human‐machine communication systems, which are essential to not only perform actions, but also obtain information especially in education applications. Moreover, any communication system has to introduce an efficient and easy way for interaction with a minimum possible error rate. The keyboard, mouse, trackball, touch‐screen, and joystick are all examples of tools which were built to provide mechanical human‐to‐machine interaction. However, a system with the ability to use oral speech, which is the natural form of communication between humans instead of mechanical communication systems, can be more practical for normal students and even a necessity for arm‐disabled students who cannot use their arms to handle traditional education tools like pens and notebooks. In this paper, we present a speech recognition system that allows arm‐disabled students to control computers by voice as a helping tool in the educational process. When a student speaks through a microphone, the speech is divided into isolated words which are compared with a predefined database of huge number of spoken words to find a match. After that, each recognized word is translated into its related tasks which will be performed by the computer like opening a teaching application or renaming a file. The speech recognition process discussed in this paper involves two separate approaches; the first approach is based on double thresholds voice activity detection and improved Mel‐frequency cepstral coefficients (MFCC), while the second approach is based on discrete wavelet transform along with modified MFCC algorithm. Utilizing the best values for all parameters in just mentioned techniques, our proposed system achieved a recognition rate of 98.7% using the first approach, and 98.86% using the second approach of which is better in ratio than the first one but slower in processing which is a critical point for a real time system. Both proposed approaches were compared with other relevant approaches and their recognition rates were noticeably higher.

show abstract

A New Steganographic Algorithm Based on Multi Directional PVD and Modified LSB

Cited by 54 publications

References 48 publications

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

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

CNN-Based Ternary Classification for Image Steganalysis

An efficient speech recognition system for arm‐disabled students based on isolated words

Contact Info

Product

Resources

About