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Deep Learning for Image Steganography and Steganalysis Challenges, Advances, and Opportunities.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep Learning for Image Steganography and Steganalysis Challenges, Advances, and Opportunities.
作者:	Yousfi, Yassine.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2022
面頁冊數:	135 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-01, Section: B.
附註:	Advisor: Fridrich, Jessica.
Contained By:	Dissertations Abstracts International84-01B.
標題:	Computer science.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29209948
ISBN:	9798834049692

Deep Learning for Image Steganography and Steganalysis Challenges, Advances, and Opportunities.
Yousfi, Yassine.

Deep Learning for Image Steganography and Steganalysis Challenges, Advances, and Opportunities. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 135 p.

Source: Dissertations Abstracts International, Volume: 84-01, Section: B.

Thesis (Ph.D.)--State University of New York at Binghamton, 2022.

This item must not be sold to any third party vendors.

Deep learning has proven incredibly successful in a plethora of fields. In computer vision, deep neural networks are now the state-of-the-art for a variety of tasks. At the first glance, steganography and steganalysis appear to be very much different tasks than classical computer vision tasks, yet deep learning, especially convolutional neural networks, popularized by the computer vision field, have outperformed all classical feature-based approaches for detecting steganography. Intuitively, steganographic embedding changes are weak, noise-like signals executed primarily in complex content, such as textures and edges. Since computer vision classifies and categorizes content, it is also suitable for detecting the presence of noise-like stego signals modulated by content.In this dissertation, we focus on refactoring steganography detectors with more modern and general components, qualitatively understanding their strengths and failure cases, and using them algorithmically to improve steganography. First, we show that many custom ingredients long believed to be necessary for successfully training a deep neural network for steganography detection can be omitted in favor of more general-purpose convolutional architectures with very few domain-specific changes. Next, we focus on understanding what makes deep neural networks superior to their classical feature-based predecessors. Lastly, we use these powerful steganography detectors as a feedback loop in novel batch steganography algorithms, which allocate more payload in images where state-of-the-art detectors fail to detect steganography.

ISBN: 9798834049692Subjects--Topical Terms:

199325
Computer science.
Subjects--Index Terms:

Deep learning

Deep Learning for Image Steganography and Steganalysis Challenges, Advances, and Opportunities.
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