An Efficient Ensemble of Convolutional Deep Steganalysis Based on Clustering

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.

Abazar Tayebe; Masjedi Peyman; TAHERI MOHAMMAD

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

Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2020 | Issue:6

Download Full-Text

View:

Download:

Information Seminar Paper

Title

An Efficient Ensemble of Convolutional Deep Steganalysis Based on Clustering

Author(s)

Abazar Tayebe | Masjedi Peyman | TAHERI MOHAMMAD

Keywords

Steganalysis

Deep Learning

K-Means

Alternate Learning

Abstract

Steganography is the task of hiding information in some media normally images. Steganalysis is the process of discriminating such instances and clean ones. In recent years, Steganalysis has tended to use Deep Learning for feature extraction and classification. Convolutional Neural Networks (CNN) have improved the Steganalysis performance but at the cost of computational complexity and memory space due to huge amount of training data. In this paper, a new framework is proposed to reduce the learning cost by a divide and conquer strategy. In the first phase, data is divided into disjoint clusters by use of K-Means. Each cluster is then fed to a separate CNN to be customized on a specific region of data space. In the final phase, the networks are merged leveraging a fast alternate-weighting process. The proposed weighting can, to some extent, compensate for reducing the size of training data per model. The experimental results show that the proposed scalable framework reduces memory and time complexity with preserving accuracy.

Video

Cites

No record.

References

No record.

Cite

APA: Copy

Abazar, Tayebe, Masjedi, Peyman, & TAHERI, MOHAMMAD. (2020). An Efficient Ensemble of Convolutional Deep Steganalysis Based on Clustering. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949254/en

Vancouver: Copy

Abazar Tayebe, Masjedi Peyman, TAHERI MOHAMMAD. An Efficient Ensemble of Convolutional Deep Steganalysis Based on Clustering. 2020. Available from: https://sid.ir/paper/949254/en

IEEE: Copy

Tayebe Abazar, Peyman Masjedi, and MOHAMMAD TAHERI, “An Efficient Ensemble of Convolutional Deep Steganalysis Based on Clustering,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2020, [Online]. Available: https://sid.ir/paper/949254/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops