scholarly journals A Novel LSB Matching Algorithm Based on Information Pre-Processing

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 8
Author(s):  
Yongjin Hu ◽  
Xiyan Li ◽  
Jun Ma
Keyword(s):  
Golden Ratio ◽  
High Capacity ◽  
Matching Function ◽  
Secret Data ◽  
Matching Algorithm ◽  
Lsb Matching ◽  
Low Distortion ◽  
Good Trade ◽  
Scanned Documents ◽  
Theory And Experiment

This paper analyzes random bits and scanned documents, two forms of secret data. The secret data were pre-processed by halftone, quadtree, and S-Box transformations, and the size of the scanned document was reduced by 8.11 times. A novel LSB matching algorithm with low distortion was proposed for the embedding step. The golden ratio was firstly applied to find the optimal embedding position and was used to design the matching function. Both theory and experiment have demonstrated that our study presented a good trade-off between high capacity and low distortion and is superior to other related schemes.

scholarly journals High-Capacity Reversible Data Hiding in Encrypted Images by Information Preprocessing

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xi-Yan Li ◽  
Xia-Bing Zhou ◽  
Qing-Lei Zhou ◽  
Shi-Jing Han ◽  
Zheng Liu
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
High Capacity ◽  
Main Idea ◽  
Original Image ◽  
Chaos Encryption ◽  
Secret Information ◽  
Encrypted Image ◽  
Encrypted Images ◽  
Scanned Documents

With the development of cloud computing, high-capacity reversible data hiding in an encrypted image (RDHEI) has attracted increasing attention. The main idea of RDHEI is that an image owner encrypts a cover image, and then a data hider embeds secret information in the encrypted image. With the information hiding key, a receiver can extract the embedded data from the hidden image; with the encryption key, the receiver reconstructs the original image. In this paper, we can embed data in the form of random bits or scanned documents. The proposed method takes full advantage of the spatial correlation in the original images to vacate the room for embedding information before image encryption. By jointly using Sudoku and Arnold chaos encryption, the encrypted images retain the vacated room. Before the data hiding phase, the secret information is preprocessed by a halftone, quadtree, and S-BOX transformation. The experimental results prove that the proposed method not only realizes high-capacity reversible data hiding in encrypted images but also reconstructs the original image completely.

scholarly journals High Embedding Capacity Data Hiding Algorithm for H.264/AVC Video Sequences without Intraframe Distortion Drift

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Dinh-Chien Nguyen ◽  
Thai-Son Nguyen ◽  
Chin-Chen Chang ◽  
Huan-Sheng Hsueh ◽  
Fang-Rong Hsu
Keyword(s):  
Digital Media ◽  
Data Hiding ◽  
Video Sequences ◽  
Embedding Capacity ◽  
Secret Data ◽  
Low Distortion ◽  
High Embedding Capacity ◽  
Advanced Video Coding ◽  
Distortion Drift ◽  
Capacity Data

Data hiding is a technique that allows secret data to be delivered securely by embedding the data into cover digital media. In this paper, we propose a new data hiding algorithm for H.264/advanced video coding (AVC) of video sequences with high embedding capacity. In the proposed scheme, to embed secret data into the quantized discrete cosine transform (QDCT) coefficients of I frames without any intraframe distortion drift, some embeddable coefficient pairs are selected in each block, and they are divided into two different groups, i.e., the embedding group and the averting group. The embedding group is used to carry the secret data, and the averting group is used to prevent distortion drift in the adjacent blocks. The experimental results show that the proposed scheme can avoid intraframe distortion drift and guarantee low distortion of video sequences. In addition, the proposed scheme provides enhanced embedding capacity compared to previous schemes. Moreover, the embedded secret data can be extracted completely without the requirement of the original secret data.

scholarly journals High-Payload Data-Hiding Method for AMBTC Decompressed Images

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 145
Author(s):  
Jung-Yao Yeh ◽  
Chih-Cheng Chen ◽  
Po-Liang Liu ◽  
Ying-Hsuan Huang
Keyword(s):  
Data Hiding ◽  
High Capacity ◽  
Cover Image ◽  
Secret Data ◽  
Perceptual Distortion ◽  
Hiding Capacity ◽  
High Data ◽  
Capacity Data ◽  
High Payload ◽  
Better Than

Data hiding is the art of embedding data into a cover image without any perceptual distortion of the cover image. Moreover, data hiding is a very crucial research topic in information security because it can be used for various applications. In this study, we proposed a high-capacity data-hiding scheme for absolute moment block truncation coding (AMBTC) decompressed images. We statistically analyzed the composition of the secret data string and developed a unique encoding and decoding dictionary search for adjusting pixel values. The dictionary was used in the embedding and extraction stages. The dictionary provides high data-hiding capacity because the secret data was compressed using dictionary-based coding. The experimental results of this study reveal that the proposed scheme is better than the existing schemes, with respect to the data-hiding capacity and visual quality.

scholarly journals Steganographic Scheme based on Message-Cover matching

2018 ◽  
Vol 8 (5) ◽  
pp. 3594
Author(s):  
Youssef Taouil ◽  
El Bachir Ameur
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Secret Message ◽  
Discrete Wavelet ◽  
Large Set ◽  
Integer Part ◽  
Least Significant Bit ◽  
Secret Data ◽  
Trade Off ◽  
Good Trade

Steganography is one of the techniques that enter into the field of information   security, it is the art of dissimulating data into digital files in an imperceptible way that does not arise the suspicion. In this paper, a steganographic method based on the Faber-Schauder discrete wavelet transform is proposed. The embedding of the secret data is performed in Least Significant Bit (LSB) of the integer part of the wavelet coefficients. The secret message is decomposed into pairs of bits, then each pair is transformed into another pair based on a permutation that allows to obtain the most matches possible between the message and the LSB of the coefficients. To assess the performance of the proposed method, experiments were carried out on a large set of images, and a comparison to prior works is accomplished. Results show a good level of imperceptibility and a good trade-off imperceptibility-capacity compared to literature.

scholarly journals High-Capacity Image Steganography with Minimum Modified Bits Based on Data Mapping and LSB Substitution

2018 ◽  
Vol 8 (11) ◽  
pp. 2199 ◽  
Author(s):  
Abdul Zakaria ◽  
Mehdi Hussain ◽  
Ainuddin Wahab ◽  
Mohd Idris ◽  
Norli Abdullah ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
High Capacity ◽  
Visual Quality ◽  
Data Mapping ◽  
Embedding Capacity ◽  
Secret Data ◽  
Stego Image ◽  
Mapping Strategy ◽  
Lsb Substitution

Steganography is the art and practice of communication using hidden messages. The least significant bits (LSB) based method is the well-known type of steganography in the spatial domain. Usually, achieving the larger embedding capacity in LSB-based methods requires a large number of LSB bits modification which indirectly reduces the visual quality of stego-image and increases the risk of steganalysis detection attacks. In this study, we propose a novel steganography method with data mapping strategy which can reduce the number of bits modification per pixel. In the proposed method, four secret data bits are mapped with the four most significant bits of a cover pixel. Furthermore, the only two LSBs of a pixel are modified to indicate the mapping strategy. Experimental results show that the proposed method is able to achieve 3.48% larger embedding capacity while enhancing the visual quality (i.e., peak signal to noise ratio (PSNR) 3.73 dB) and reducing the modification of 0.76 bits per pixel. Moreover, the proposed method provides security against basic Regular and Singular groups (RS) steganalysis and histogram steganalysis detection attacks.

scholarly journals High Capacity Reversible Data Hiding Based on the Compression of Pixel Differences

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1435
Author(s):  
Kai-Meng Chen
Keyword(s):  
Spatial Correlation ◽  
Data Hiding ◽  
Reversible Data Hiding ◽  
High Capacity ◽  
Visual Quality ◽  
Small Range ◽  
Encryption Scheme ◽  
Secret Data ◽  
Encrypted Image ◽  
Block Level

In this paper, we proposed a novel reversible data hiding method in encrypted image (RDHEI), which is based on the compression of pixel differences. In the proposed method, at the content owner’ side the image is divided into non-overlapping blocks, and a block-level image encryption scheme is used to generate the encrypted image, which partially retains spatial correlation in the blocks. Due to the spatial correlation, in each block the pixels are highly likely to be similar. Therefore, the pixel differences in all blocks are concentrated in a small range and can be compressed. By the compression of pixel differences, the data hider can vacate the room to accommodate secret data in the encrypted image without losing information. At the receiver’s side, the receiver can obtain secret data or retrieve the original image using different keys with no error. The experimental results demonstrate that, compared with existing methods, the proposed method can achieve a higher capacity and visual quality.

Block-Based High Capacity Multilevel Image Steganography

2016 ◽  
Vol 25 (08) ◽  
pp. 1650091 ◽  
Author(s):  
Geeta Kasana ◽  
Kulbir Singh ◽  
Satvinder Singh Bhatia
Keyword(s):  
Digital Images ◽  
High Capacity ◽  
Block Size ◽  
Visual Quality ◽  
Cover Image ◽  
Embedding Capacity ◽  
Secret Data ◽  
Block Based ◽  
High Embedding Capacity

This paper proposes a block-based high capacity steganography technique for digital images. The cover image is decomposed into blocks of equal size and the largest pixel of each block is found to embed the secret data bits and also the smallest pixel of each block is used for embedding to enhance the capacity. Embedding of secret data is performed using the concept that the pixel of a cover image has only two states — even and odd. Multilevel approach is also combined in the proposed technique to achieve high embedding capacity. In order to make the proposed technique more secure, a key is generated using embedding levels, block size, pixel embedding way, encryption parameters, and starting blocks of each embedding levels. Embedding capacity and visual quality of stego images generated by the proposed steganography technique are higher than the existing techniques. Steganalysis tests have been performed to show the un-detectability and imperceptibility of the proposed technique.

scholarly journals Steganography in Color Animated Image Sequence for Secret Data Sharing Using Secure Hash Algorithm

2021 ◽  
Author(s):  
Ratan Kumar Basak ◽  
Ritam Chatterjee ◽  
Paramartha Dutta ◽  
Kousik Dasgupta
Keyword(s):  
High Capacity ◽  
Text Messages ◽  
Secret Message ◽  
Large Set ◽  
Secret Data ◽  
Secret Information ◽  
Hash Algorithm ◽  
Secure Hash Algorithm ◽  
High Embedding Capacity ◽  
Validation Scheme

Abstract This paper presents a high capacity steganographic approach with secret message validation scheme at the receiver end. The proposed idea develops specifically for animated GIF, the cover media, to conceal secret text messages where Least Significant Digit (LSD) method is employed to embed secret information in the form of ASCII value. To validate the secret information at the receiver end, the secret text is encoded with Secure Hash Algorithm-1(SHA1) which is subsequently embedded in certain pre-defined portion of the cover media. The proposed algorithm is experimented on a large set of colored animated image sequences by varying text messages which produces satisfactory results. The proposed method also maintains good visual perceptibility while securing high embedding capacity

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