ADAPTIVE DIGITAL IMAGE WATERMARKING BASED ON MACHINE LEARNING

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

NAFISIFARD PAYMAN; DERHAMI VALI; LATIF ALIMOHAMMAD

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING) Year:2012 | Volume:2 | Issue:4 (5) Page(s): 47-64

Download Full-Text

Persian Verion

View:

1,571

Download:

Cites:

Information Journal Paper

Title

ADAPTIVE DIGITAL IMAGE WATERMARKING BASED ON MACHINE LEARNING

Author(s)

NAFISIFARD PAYMAN | DERHAMI VALI | LATIF ALIMOHAMMAD | Issue Writer Certificate

Keywords

ADAPTIVE DIGITAL IMAGE WATERMARKINGQ3

HUMAN VISUAL SYSTEMQ2

SUPPORT VECTOR MACHINEQ2

GENETIC ALGORITHMQ2

Abstract

With the widespread use of internet and the development in computer industry, copyright protection of digital multimedia has become a serious issue. Digital watermarking is an appropriate way to solve this problem. This paper proposes a novel adoptive image watermarking scheme in discrete cosine transform (DCT) domain. The host image is divided in to non-overlapping image blocks with size 8×8 and then the binary watermark bits are embedded into DCT domain of image blocks. The embedding locations are selected randomly to enhance the security of watermark. Selected embedding blocks are classified into six classes based on their characteristics like luminance, texture and nearness to the edges. In this research SUPPORT VECTOR MACHINE (SVM) is used to simulate HUMAN VISUAL SYSTEM (HVS) in order to classify the blocks. Then optimal watermark strengths are determined to different classes by GENETIC ALGORITHM for embedding adaptively. The experimental results show that the proposed method has good fidelity and robustness against different attacks.

Cites

No record.

References

No record.

Cite

APA: Copy

NAFISIFARD, PAYMAN, DERHAMI, VALI, & LATIF, ALIMOHAMMAD. (2012). ADAPTIVE DIGITAL IMAGE WATERMARKING BASED ON MACHINE LEARNING. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), 2(4 (5)), 47-64. SID. https://sid.ir/paper/202997/en

Vancouver: Copy

NAFISIFARD PAYMAN, DERHAMI VALI, LATIF ALIMOHAMMAD. ADAPTIVE DIGITAL IMAGE WATERMARKING BASED ON MACHINE LEARNING. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING)[Internet]. 2012;2(4 (5)):47-64. Available from: https://sid.ir/paper/202997/en

IEEE: Copy

PAYMAN NAFISIFARD, VALI DERHAMI, and ALIMOHAMMAD LATIF, “ADAPTIVE DIGITAL IMAGE WATERMARKING BASED ON MACHINE LEARNING,” COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), vol. 2, no. 4 (5), pp. 47–64, 2012, [Online]. Available: https://sid.ir/paper/202997/en

Related Journal Papers