A differential evolution based algorithm for breaking the visual steganalytic system

Shih, Frank Y.; Edupuganti, Venkata Gopal

doi:10.1007/s00500-008-0330-z

Cited by 13 publications

(2 citation statements)

References 7 publications

(5 reference statements)

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“…DE was proposed by Storn and Price Price 1995, 1997), and since then it has been used in many practical cases because of its efficiency and simplicity. The original DE was modified and many new versions have been proposed (Brest et al , 2007Brest and Maučec 2008;Teo 2006;Qin et al 2009;Shih and Edupuganti 2009;Teng et al 2009;Caponio et al 2009;Quian et al 2009). Original DE contains two populations, both containing N p vectors x i ; i ¼ 1; 2; .…”

Section: Differential Evolutionmentioning

confidence: 98%

History mechanism supported differential evolution for chess evaluation function tuning

et al. 2010

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This paper presents a differential evolution (DE) based approach to chess evaluation function tuning. DE with opposition-based optimization is employed and upgraded with a history mechanism to improve the evaluation of individuals and the tuning process. The general idea is based on individual evaluations according to played games through several generations and different environments. We introduce a new history mechanism which uses an auxiliary population containing good individuals. This new mechanism ensures that good individuals remain within the evolutionary process, even though they died several generations back and later can be brought back into the evolutionary process. In such a manner the evaluation of individuals is improved and consequently the whole tuning process.

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Section: Differential Evolutionmentioning

confidence: 98%

History mechanism supported differential evolution for chess evaluation function tuning

et al. 2010

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“…DE has been successfully applied to solve a wide range of real-life application problems like image classification, IIR filter design etc. (Shih and Edupuganti 2009;Omran et al 2005a;Storn 1995) and has reportedly outperformed several other optimization techniques (Vesterstroem and Thomsen 2004;Andre et al 2001;Hrstka and Kucerová 2004).…”

Section: Introductionmentioning

confidence: 97%

Improving the performance of differential evolution algorithm using Cauchy mutation

Ali

Pant

2010

Soft Comput

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Differential evolution (DE) is a powerful yet simple evolutionary algorithm for optimization of realvalued, multimodal functions. DE is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from premature convergence and/or slow convergence rate resulting in poor solution quality and/or larger number of function evaluation resulting in large CPU time for optimizing the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This research introduces a modified differential evolution (MDE) that enhances the convergence rate without compromising with the solution quality. The proposed MDE algorithm maintains a failure_counter (FC) to keep a tab on the performance of the algorithm by scanning or monitoring the individuals. Finally, the individuals that fail to show any improvement in the function value for a successive number of generations are subject to Cauchy mutation with the hope of pulling them out of a local attractor which may be the cause of their deteriorating performance. The performance of proposed MDE is investigated on a comprehensive set of 15 standard benchmark problems with varying degrees of complexities and 7 nontraditional problems suggested in the special session of CEC2008. Numerical results and statistical analysis show that the proposed modifications help in locating the global optimal solution in lesser numbers of function evaluation in comparison with basic DE and several other contemporary optimization algorithms.

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Image steganography and steganalysis

Bachrach

Shih

2011

WIREs Computational Stats

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Image steganography is used to embed covert messages in the form of files, text, or other images in digital images. The intent is to transmit hidden information. Steganalysis is the process used to detect hidden messages in images. Although steganography is not a new discipline, it has become increasingly important in today's digital world where information is often and easily exchanged through the Internet, email, and other means using computers. The need for better methods and techniques which can be used both to embed hidden information in images and to detect messages hidden in images is driving new research in the area of steganography and steganalysis. This article surveys image steganography and steganalysis. The aim is to introduce the uninformed reader to image steganography and steganalysis. The key concepts behind image steganography and steganalysis are explained. The history and origin of steganography are outlined. Steganography is compared with watermarking in technique and intent. Details of how images are represented are explained. Methods and algorithms used to embed messages in images and to detect embedded messages are explored. Currently available steganography and steganalysis tools are explained. A demonstration of how to embed hidden information in an image using available steganography tools is provided.

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A differential evolution based algorithm for breaking the visual steganalytic system

Cited by 13 publications

References 7 publications

History mechanism supported differential evolution for chess evaluation function tuning

History mechanism supported differential evolution for chess evaluation function tuning

Improving the performance of differential evolution algorithm using Cauchy mutation

Image steganography and steganalysis

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