Develop a Detection System for Grey and ColourStego Images

Aljarf, Ahd; Amin, Saad; Filippas, John; Shuttelworth, James

doi:10.7763/ijmo.2013.v3.320

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…The results mention that the suggested algorithm is effectively more than several GIF steganography algorithms and steganography tools. In [8] Aljarf, Amin, Filippas, and Shuttelworth proposed a steganography detection system for both color and gray images based on four features which are homogeneity, correlation, contrast, and energy. Using grey images for steganography has many limitations.…”

Section: Literature Reviewmentioning

confidence: 99%

Image Steganalysis in Frequency Domain Using Co-Occurrence Matrix and Bpnn

Khalifa¹,

Zeebaree²,

Atas³

et al. 2019

SJUOZ

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In the last two decades, steganalysis has become a fertile research area to minimize the security risks left behind by Misuse of data concealment in digital computer files. As the propagation of hidden writing increased, the need for the steganalysis emerged and grew to a large extent necessary to deter illicit secret communications. This paper introduces a steganalysis system to detect hidden information in images through using co-occurrence matrix, frequency domain transform, the first three moments, and back propagation neural network (BPNN). Four varieties of the system implemented. Firstly, the co-occurrence matrix calculated for the input image, which suspected to be a carrier of hidden secret information. Second, three levels of discrete wavelet transform (DWT) are applied resulting in 12 subbands. Then, those subbands along with the original image are transformed by discrete Fourier transform (DFT) or discrete cosine transform (DCT) to produce 13 subbands. After that, the first three moments are calculated resulting feature vector with 39 features. Finally, BPNN is used as a classifier to determine whether the image is containing hidden information or not. The system is tested with and without co-occurrence matrix, each of them once using DFT and another time using DCT. The results have shown that using co-occurrence matrix with DFT has the highest performance, which was 81.82% on the Hiding Ratio of 0.5 bit per pixel. This work demonstrates a good effect comparing to previous works.

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Section: Literature Reviewmentioning

confidence: 99%

Image Steganalysis in Frequency Domain Using Co-Occurrence Matrix and Bpnn

Khalifa¹,

Zeebaree²,

Atas³

et al. 2019

SJUOZ

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“…Co-occurrence matrix [13] depict the correlation between wavelet coefficients. We have extracted contrast, correlation, energy and homogeneity [18] features from Co-occurrence matrix from 1Image+12 subbands of DWT of an image obtaining 52 features.…”

Section: Co-occurrencesmentioning

confidence: 99%

DWT Feature based Blind Image Steganalysis using Neural Network Classifier

Saini¹,

Chhikara²

2015

IJERT

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The objective of forensic steganalysis is to identify the existence of embedded message and to ultimately retrieve the secret message. In this paper, we have extracted Histogram, Markov and Co-occurrence features from wavelet domain and compared with existing farid 72 DWT features. The performance metrics are MSE (Mean square error) and classification accuracy for blind image steganalysis using two steganography algorithms outguess and nsF5. Neural network back propagation classifier has been used to classify images into stego images and clean images. Experimental results show that the proposed features considerably outperform existing farid 72 DWT features.

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