Steganalysis Using KL Transform and Radial Basis Neural Network

Hasoon, Safwan O.; Khalifa, Farhad M.

doi:10.33899/csmj.2012.163670

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Due to the fast growth of internet and boundless use of image processing and multimedia technologies, People can easily obtain the extensive multimedia files through the internet, create problems such as prohibited copying, forge ownership claims, because of the ease of copying or altering digital documents quickly and without quality loss (Saboori and Hosseini, 2014). The watermarking system has been proposed for avoiding these problems (Hasoon and Khalifa, 2012;Hosseini and Saboori, 2015).…”

Section: Introductionmentioning

confidence: 99%

Image Watermarking Using All Phase Discrete Cosine Biorthogonal Transform in Selected Pixel Blocks

Khalifa

Saeed

2020

Polytechnic j.

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In the past decade, a transform called all phase discrete cosine biorthogonal transform (APDCBT) appeared in the field of digital image processing. It is mainly used to solve the negativity found in discrete cosine transform (DCT), especially in a low bit rate. In this paper, the APDCBT is employed for watermark insertion based on selected regions of an image. The insertion is depending on the homogeneity of each part of the image. To determine the extent of homogeneity, there are two criteria: Mean and standard deviation are applied for the intensity of the image. Medium frequency bands of APDCBT in image pixel blocks are used to hold the embedded watermark. Then, the transform is inversed to obtain the resultant watermarked image. The robustness of APDCBT against watermark removal attacks is tested and the experimental results showed the superiority of the APDCBT over traditional DCT in the watermark embedding system. More specifically, when the LSB reset attack applied. For instance, when the contrast adjustment attack applied, the average of normalized cross-correlation (NCC) values of extracted watermark images with the original watermark, it was 0.992 for the proposed method. This is a promised result, if it is compared with the NCC of the DCT method, which was 0.423. The proposed method can be used for copyright protection purpose.

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Section: Introductionmentioning

confidence: 99%

Image Watermarking Using All Phase Discrete Cosine Biorthogonal Transform in Selected Pixel Blocks

Khalifa

Saeed

2020

Polytechnic j.

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“…It was the beginning of the science of Steganalysis. Thus opening the wide door for researchers to engage in the fields of hidden writing and steganalysis, and it was a fertile field of scientific research fields and produced countless methods in both fields [3]. Steganalysis is the technique and science that is used to decide if the messages are involved in the image or not by the steganography algorithm.…”

Section: Introductionmentioning

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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Image steganalysis using deep learning: a systematic review and open research challenges

Farooq

Selwal

2023

J Ambient Intell Human Comput

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Steganalysis Using KL Transform and Radial Basis Neural Network

Cited by 4 publications

References 8 publications

Image Watermarking Using All Phase Discrete Cosine Biorthogonal Transform in Selected Pixel Blocks

Image Watermarking Using All Phase Discrete Cosine Biorthogonal Transform in Selected Pixel Blocks

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

Image steganalysis using deep learning: a systematic review and open research challenges

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