Image Steganography Using Gradient Adjacent Prediction in Side-Match Vector Quantization

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/106874

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106874

Title:	Image Steganography Using Gradient Adjacent Prediction in Side-Match Vector Quantization
Authors:	王尉任;Tsui, Shiau-Rung;Huang, Cheng-Ta;Wang, Wei-Jen
Contributors:	資訊電機學院資訊工程學系
Keywords:	Artificial intelligence;Gradient adjacent prediction (GAP);SMVQ;Steganography
Date:	2013-06-28
Issue Date:	2026-04-23 13:47:24 (UTC+8)
Publisher:	Springer Verlag;Germany: Springer Berlin / Heidelberg
Abstract:	摘要： This study presents a new steganographic method that embeds secret data into a cover digital image using VQ encoding. The core concept of the proposed method uses the gradient adjacent prediction (GAP) algorithm, which enhances prediction accuracy of neighboring blocks in SMVQ encoding. To embed secret data into the cover image, the proposed method utilizes the features of GAP to decide the capacity of the secret data per pixel in a block. It then embeds the secret data accordingly. It also embeds an index value in each block to ensure that the secret data can be recovered back. The index value points to the closest codeword of a state codebook to the encoding block, where the state codebook is generated by GAP-based SMVQ. The result shows that the proposed method has better performance than a recent similar work proposed by Chen and Lin in 2010. 出版者： Germany: Springer Berlin / Heidelberg 出版日期： 2013 出處： Advances in Intelligent Systems and Applications - Volume 2, 2013, p.121-129 版權： Springer-Verlag Berlin Heidelberg 2013 識別號： ISSN: 2190-3018 識別號： ISBN: 9783642354724 識別號： ISBN: 3642354726 識別號： EISSN: 2190-3026 識別號： EISBN: 9783642354731 識別號： EISBN: 3642354734 識別號： DOI: 10.1007/978-3-642-35473-1_13 識別號： OCLC: 823728185 識別號： LCCallNum: Q342
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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