A Study on Embedding Efficiency of Matrix Encoding

2012 ◽  
Vol 4 (1) ◽  
pp. 37-48
Author(s):  
Lifang Yu ◽  
Yun Q. Shi ◽  
Yao Zhao ◽  
Rongrong Ni ◽  
Gang Cao
Keyword(s):  
Embedding Efficiency ◽  
Matrix Embedding ◽  
Matrix Encoding

In this paper, the authors examine embedding efficiency, which influences the most concerned performance of steganography, security, directly. Embedding efficiency is defined as the number of random message bits embedded per embedding change. Recently, matrix embedding has gained extensive attention because of its outstanding performance in boosting steganographic schemes’ embedding efficiency. Firstly, the authors evaluate embedding change not only on the number of changed coefficients but also on the varying magnitude of changed coefficients. Secondly, embedding efficiency of matrix embedding with different radixes is formularized and investigated. A conclusion is drawn that ternary matrix embedding can achieve the highest embedding efficiency.

A Study on Embedding Efficiency of Matrix Encoding

2013 ◽  
pp. 92-102
Author(s):  
Lifang Yu ◽  
Yun Q. Shi ◽  
Yao Zhao ◽  
Rongrong Ni ◽  
Gang Cao
Keyword(s):  
Embedding Efficiency ◽  
Matrix Embedding ◽  
Matrix Encoding

In this paper, the authors examine embedding efficiency, which influences the most concerned performance of steganography, security, directly. Embedding efficiency is defined as the number of random message bits embedded per embedding change. Recently, matrix embedding has gained extensive attention because of its outstanding performance in boosting steganographic schemes’ embedding efficiency. Firstly, the authors evaluate embedding change not only on the number of changed coefficients but also on the varying magnitude of changed coefficients. Secondly, embedding efficiency of matrix embedding with different radixes is formularized and investigated. A conclusion is drawn that ternary matrix embedding can achieve the highest embedding efficiency.

scholarly journals An Improved Product Code-Based Data Hiding Scheme

2018 ◽  
Vol 8 (11) ◽  
pp. 2119
Author(s):  
Wen-Rong Zhang ◽  
Yuh-Ming Huang
Keyword(s):  
Data Hiding ◽  
The Other ◽  
Two Dimensional ◽  
Optimal Solutions ◽  
Approximation Approach ◽  
One Dimensional ◽  
Product Code ◽  
Embedding Efficiency ◽  
Matrix Embedding ◽  
Embedding Rate

This paper explores the data hiding schemes which are based on the principle of matrix embedding. Under the same embedding rate, the efficiency of each data hiding scheme is evaluated by the metric of average embedding efficiency. In the literature, both the row-column embedding and the weight approximation embedding algorithms are sub-optimal solutions for the product code-based data hiding problem. For the former, it is still based on the concept of one-dimensional (1-D) toggle syndrome, and the concept of two-dimensional (2-D) toggle syndrome is directly adopted for the latter one. Data hiding with multiple embedding channels is the practice of hiding messages into hidden media many times. Here, two multi-channel embedding-based data hiding techniques—one is the 1-D toggle syndrome-based embedding scheme (1DTS-1), and the other is the improved weight approximation-based embedding scheme (2DTS-1), are presented. In the former, the proposed one-off decision technique is used to determine the locations of the required modification bits, and the amount of modification will be reduced through utilizing the characteristics of the linear code. With the technique of the former, in the latter, the amount of modification bits can be further reduced because that a toggle array with better structure is generated, which is more suitable for being assigned as the initial toggle array while applying the weight approximation approach. The experimental results show our proposed hybrid 1-D/2-D toggle syndrome-based embedding scheme (2DTS-1) has increased the embedding efficiency by 0.1149 when compared to the weight approximation embedding algorithm. Further, the embedding efficiency of the latter one can be further and significantly enhanced through the Hamming+1 technique.

An extended matrix encoding algorithm for steganography of high embedding efficiency

2011 ◽  
Vol 37 (6) ◽  
pp. 973-981 ◽  
Author(s):  
Li Fan ◽  
Tiegang Gao ◽  
Qunting Yang ◽  
Yanjun Cao
Keyword(s):  
Embedding Efficiency ◽  
Matrix Encoding ◽  
Extended Matrix

Textual Integrity Detection Algorithm Based on Matrix Encoding

2012 ◽  
Vol 263-266 ◽  
pp. 2369-2372
Author(s):  
Sheng Jun Su ◽  
Jin Jin Xu ◽  
Shuo Zhong Wang
Keyword(s):  
Digital Watermark ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Embedding Efficiency ◽  
Matrix Encoding ◽  
Word Document

A novel textual integrity detection algorithm based on matrix coding is presented in this paper, which can improve the embedding efficiency and security of digital watermark in the WORD document. The experimental results show that better invisibility and security of watermark can be obtained by using matrix coding.

scholarly journals An Embedding Strategy Using Q-Ary Convolutional Codes for Large and Small Payloads

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1577
Author(s):  
Jyun-Jie Wang ◽  
Chi-Yuan Lin ◽  
Sheng-Chih Yang ◽  
Hsi-Yuan Chang ◽  
Yin-Chen Lin
Keyword(s):  
Convolutional Code ◽  
Convolutional Codes ◽  
Block Codes ◽  
Maximum Likelihood Decoding ◽  
Linear Block Codes ◽  
Embedding Efficiency ◽  
Matrix Embedding ◽  
Main Disadvantage ◽  
Embedding Strategy ◽  
Embedding Rate

Matrix embedding (ME) code is a commonly used steganography technique, which uses linear block codes to improve embedding efficiency. However, its main disadvantage is the inability to perform maximum likelihood decoding due to the high complexity of decoding large ME codes. As such, it is difficult to improve the embedding efficiency. The proposed q-ary embedding code can provide excellent embedding efficiency and is suitable for various embedding rates (large and small payloads). This article discusses that by using perforation technology, a convolutional code with a high embedding rate can be easily converted into a convolutional code with a low embedding rate. By keeping the embedding rate of the (2, 1) convolutional code unchanged, convolutional codes with different embedding rates can be designed through puncturing.

A robust and high capacity data hiding method for JPEG compressed images with SVD-based block selection and advanced error correcting techniques

2021 ◽  
pp. 1-11
Author(s):  
Kusan Biswas
Keyword(s):  
Data Hiding ◽  
High Capacity ◽  
Error Correcting Codes ◽  
Human Vision ◽  
Data Embedding ◽  
Compressed Images ◽  
Matrix Encoding ◽  
Dct Coefficients ◽  
Capacity Data ◽  
Value Decomposition

In this paper, we propose a frequency domain data hiding method for the JPEG compressed images. The proposed method embeds data in the DCT coefficients of the selected 8 × 8 blocks. According to the theories of Human Visual Systems  (HVS), human vision is less sensitive to perturbation of pixel values in the uneven areas of the image. In this paper we propose a Singular Value Decomposition based image roughness measure (SVD-IRM) using which we select the coarse 8 × 8 blocks as data embedding destinations. Moreover, to make the embedded data more robust against re-compression attack and error due to transmission over noisy channels, we employ Turbo error correcting codes. The actual data embedding is done using a proposed variant of matrix encoding that is capable of embedding three bits by modifying only one bit in block of seven carrier features. We have carried out experiments to validate the performance and it is found that the proposed method achieves better payload capacity and visual quality and is more robust than some of the recent state-of-the-art methods proposed in the literature.

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