scholarly journals High Capacity Image Steganography using Pixel Value Differencing Method with Data Compression using Neural Network

2019 ◽  
Vol 8 (12) ◽  
pp. 1800-1804
Keyword(s):  
Neural Network ◽  
Data Compression ◽  
Data Hiding ◽  
Image Steganography ◽  
Data Embedding ◽  
Least Significant Bit ◽  
Secret Data ◽  
Stego Image ◽  
Compression Technique ◽  
Hiding Capacity

The Digital Market Is Rapidly Growing Day By Day. So, Data Hiding Is Going To Increase Its Importance. Information Can Be Hidden In Different Embedding Mediums, Known As Carriers By Using Steganography Techniques. The Carriers Are Different Multimedia Medium Such As Images, Audio Files, Video Files, And Text Files .There Are Several Techniques Present To Achieve Data Hiding Like Least Significant Bit Insertion Method And Transform Domain Technique. The Data Hidden Capacity Inside The Cover Image Totally Depends On The Properties Of The Image Like Number Of Noisy Pixels. Data Compression Provides To Hide Large Amount Of Secret Data To Increase The Capacity And The Image Steganography Based On Any Neural Network Provides That The Size And Quality Of The Stego-Image Remains Unaltered After Data Embedding. In This Paper We Propose A New Method Combined With Data Compression Along With Data Embedding Technique And After Embedding To Maintain The Quality The Communication Channel Use The Neural Network. The Compression Technique Increase The Data Hiding Capacity And The Use Of Neural Network Maintain The Flow Of Data Processing Signal

scholarly journals Image Steganography using Improved Lsb-Mapping Technique with Enhanced Recovery Speed

2019 ◽  
Vol 8 (4) ◽  
pp. 11473-11478
Keyword(s):  
Data Hiding ◽  
Enhanced Recovery ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Cover Image ◽  
Mapping Technique ◽  
Secret Data ◽  
Logical Function ◽  
Hiding Capacity ◽  
Carrier Image

In recent days, for sending secret messages, we require secure internet. Image steganography is considered as the eminent tool for data hiding which provides better security for the data transmitted over internet. In the proposed work, the payload data is embedded using improved LSB-mapping technique. In this approach, two bits from each pixel of carrier image are considered for mapping and addition. Two bits of payload data can be embedded in one cover image pixel hence enhanced the hiding capacity. A logical function on addition is applied on 1st and 2nd bits of cover image pixel, and a mapping table is constructed which gives solution for data hiding and extraction. Simple addition function on stego pixel is performed to extract payload data hence increases the recovery speed. Here the secret data is not directly embedded but instead mapped and added with a number using modulo-4 strategy. Hence the payload data hidden using proposed approach provide more security and it can resist against regular LSB decoding approaches. The proposed work is implemented and tested for several gray scale as well as color images and compared with respect to parameters like peak signal to noise ratio and MSE. The proposed technique gives better results when compared and histogram of cover and stego images are also compared.

scholarly journals High Security and Capacity of Image Steganography for Hiding Human Speech Based on Spatial and Cepstral Domains

2020 ◽  
Vol 8 (1) ◽  
pp. 95
Author(s):  
Yazen A. Khaleel
Keyword(s):  
Speech Signal ◽  
Vocal Tract ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Mel Frequency Cepstral Coefficients ◽  
Secret Data ◽  
Stego Image ◽  
Hiding Capacity ◽  
Original Speech

A new technique of hiding a speech signal clip inside a digital color image is proposed in this paper to improve steganography security and loading capacity. The suggested technique of image steganography is achieved using both spatial and cepstral domains, where the Mel-frequency cepstral coefficients (MFCCs) are adopted, as very efficient features of the speech signal. The presented technique in this paper contributes to improving the image steganography features through two approaches. First is to support the hiding capacity by the usage of the extracted MFCCs features and pitches extracted from the speech signal and embed them inside the cover color image rather than directly hiding the whole samples of the digitized speech signal. Second is to improve the data security by hiding the secret data (MFCCs features) anywhere in the host image rather than directly using the least significant bits substitution of the cover image. At the recovering side, the proposed approach recovers these hidden features and using them to reconstruct the speech waveform again by inverting the steps of MFCCs extraction to recover an approximated vocal tract response and combine it with recovered pitch based excitation signal. The results show a peak signal to noise ratio of 52.4 dB of the stego-image, which reflect a very good quality and a reduction ratio of embedded data to about (6%–25%). In addition, the results show a speech reconstruction degree of about 94.24% correlation with the original speech signal.

scholarly journals Steganography using Spatial Domain Techniques

2019 ◽  
Vol 8 (4) ◽  
pp. 3369-3373
Keyword(s):  
Data Security ◽  
Data Transfer ◽  
Spatial Domain ◽  
Secret Message ◽  
Least Significant Bit ◽  
Secret Data ◽  
Stego Image ◽  
Hiding Capacity ◽  
Military Organization ◽  
Redundant Data

In present world data transfer using the internet is growing. It is very easy and fast way to transfer information like confidential documents, economic transactions, business applications and other covert information over internet. With the advent and growth of internet, people are more concerned about security of information. Data Security is important while data is transferred over internet because any illegal user can access important and private data also make it worthless. Research in data security area will help government agencies, military organization and private companies to securely transmit their confidential data over internet. From past few years various steganography techniques have been developed to hide secret message using various multimedia objects having large amount of redundant data to support steganography. In this paper introduction about steganography, related concepts and implementation of commonly used spatial domain techniques like LSB(Least Significant Bit Technique) with modulus, PVD(Pixel Value Difference) with LSB replacement and adaptive data hiding over edges with LSB are considered. It is observed(while visual, statistical analysis and experiments were carried out) with benchmark cover and stego objects that embedding same amount of secret data in each pixel leads to more visible distortions in a stego image because all pixels do not bear same amount of changes and this effect is more observed in smooth area then edges. Improving stego image imperceptibility and adjusting hiding capacity adaptively are major related research challenges about spatial domain techniques.

scholarly journals A New Reversible Data Hiding Technique Combined with Interpolation and Pixel Value Differencing Method

2020 ◽  
pp. 53-58
Author(s):  
Jayeeta Majumder ◽  
Chittaranjan Pradhan
Keyword(s):  
High Capacity ◽  
Main Idea ◽  
Image Steganography ◽  
Second Phase ◽  
Secret Data ◽  
Stego Image ◽  
Hiding Capacity ◽  
Small Distortion ◽  
Pixel Value ◽  
Pixel Value Differencing

Steganography is the popular security method that provides complete security for communicating secret details. Image steganography is a very interesting field because of the imperceptible way of hiding data in images, since small distortion in the images cannot be identified by a human eye. This is the main idea to develop image steganography algorithms to improve visual quality. Pixel Value Differencing is able to provide a high quality stego image in spite of the high capacity of the concealed information. This paper proposes the first the interpolation techniques with the pixel block then applying then the Pixel Value Differencing method. Here in the first phase the original image is portioned into 2X2 block then applying the nearest neighbour interpolation technique after that in the second phase PVD is used to embed the secret data. Then the new pixel value of the neighbouring pixel also calculated. In this paper one variant are proposed by using single range table. We observed that for both the variant PSNR value and the hiding capacity are increased.

scholarly journals Image steganography using least significant bit and secret map techniques

2020 ◽  
Vol 10 (1) ◽  
pp. 935
Author(s):  
Ashwaq Alabaichi ◽  
Maisa'a Abid Ali K. Al-Dabbas ◽  
Adnan Salih
Keyword(s):  
Quality Index ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Least Significant Bit ◽  
Secret Data ◽  
Hiding Capacity ◽  
Image Histogram ◽  
Image Fidelity ◽  
The Media ◽  
Key Sensitivity

In steganography, secret data are invisible in cover media, such as text, audio, video and image. Hence, attackers have no knowledge of the original message contained in the media or which algorithm is used to embed or extract such message. Image steganography is a branch of steganography in which secret data are hidden in host images. In this study, image steganography using least significant bit and secret map techniques is performed by applying 3D chaotic maps, namely, 3D Chebyshev and 3D logistic maps, to obtain high security. This technique is based on the concept of performing random insertion and selecting a pixel from a host image. The proposed algorithm is comprehensively evaluated on the basis of different criteria, such as correlation coefficient, information entropy, homogeneity, contrast, image, histogram, key sensitivity, hiding capacity, quality index, mean square error (MSE), peak signal-to-noise ratio (PSNR) and image fidelity. Results show that the proposed algorithm satisfies all the aforementioned criteria and is superior to other previous methods. Hence, it is efficient in hiding secret data and preserving the good visual quality of stego images. The proposed algorithm is resistant to different attacks, such as differential and statistical attacks, and yields good results in terms of key sensitivity, hiding capacity, quality index, MSE, PSNR and image fidelity.

scholarly journals Generalized Reversible Data Hiding with Content-Adaptive Operation and Fast Histogram Shifting Optimization

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 917
Author(s):  
Limengnan Zhou ◽  
Hongyu Han ◽  
Hanzhou Wu
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Hot Spot ◽  
Data Embedding ◽  
Histogram Shifting ◽  
Secret Data ◽  
Content Selection ◽  
Wide Range ◽  
Map Generation ◽  
Content Adaptive

Reversible data hiding (RDH) has become a hot spot in recent years as it allows both the secret data and the raw host to be perfectly reconstructed, which is quite desirable in sensitive applications requiring no degradation of the host. A lot of RDH algorithms have been designed by a sophisticated empirical way. It is not easy to extend them to a general case, which, to a certain extent, may have limited their wide-range applicability. Therefore, it motivates us to revisit the conventional RDH algorithms and present a general framework of RDH in this paper. The proposed framework divides the system design of RDH at the data hider side into four important parts, i.e., binary-map generation, content prediction, content selection, and data embedding, so that the data hider can easily design and implement, as well as improve, an RDH system. For each part, we introduce content-adaptive techniques that can benefit the subsequent data-embedding procedure. We also analyze the relationships between these four parts and present different perspectives. In addition, we introduce a fast histogram shifting optimization (FastHiSO) algorithm for data embedding to keep the payload-distortion performance sufficient while reducing the computational complexity. Two RDH algorithms are presented to show the efficiency and applicability of the proposed framework. It is expected that the proposed framework can benefit the design of an RDH system, and the introduced techniques can be incorporated into the design of advanced RDH algorithms.

scholarly journals Taylor expansion based stereo image reversible data hiding

2020 ◽  
Author(s):  
Xinyang Ying ◽  
Guobing Zhou
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Taylor Expansion ◽  
Stereo Image ◽  
Embedding Capacity ◽  
Stereo Images ◽  
Secret Data ◽  
Stego Image ◽  
Multi Level

Abstract The reversible data hiding allows original image to be completely recovered from the stego image when the secret data has been extracted, it is has drawn a lot of attentions from researchers. In this paper, a novel Taylor Expansion (TE) based stereo image reversible data hiding method is presented. Since the prediction accuracy is essential to the data hiding performance, a novel TE based predictor using correlations of two views of the stereo image is proposed. TE can fully exploit strong relationships between matched pixels in the stereo image so that the accuracy of the prediction can be improved. Then, histogram shifting is utilized to embed data to decrease distortion of stereo images, and multi-level hiding can increase embedding capacity. Experimental results show that the proposed method is superior to some existing data hiding methods considering embedding capacity and the quality of the stego stereo images.

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 SteganoCNN: Image Steganography with Generalization Ability Based on Convolutional Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1140
Author(s):  
Xintao Duan ◽  
Nao Liu ◽  
Mengxiao Gou ◽  
Wenxin Wang ◽  
Chuan Qin
Keyword(s):  
Neural Network ◽  
Real Life ◽  
Image Steganography ◽  
Aerial Images ◽  
Remote Sensing Images ◽  
Data Types ◽  
Stego Image ◽  
Payload Capacity ◽  
Carrier Image ◽  
Entire Network

Image-to-image steganography is hiding one image in another image. However, hiding two secret images into one carrier image is a challenge today. The application of image steganography based on deep learning in real-life is relatively rare. In this paper, a new Steganography Convolution Neural Network (SteganoCNN) model is proposed, which solves the problem of two images embedded in a carrier image and can effectively reconstruct two secret images. SteganoCNN has two modules, an encoding network, and a decoding network, whereas the decoding network includes two extraction networks. First, the entire network is trained end-to-end, the encoding network automatically embeds the secret image into the carrier image, and the decoding network is used to reconstruct two different secret images. The experimental results show that the proposed steganography scheme has a maximum image payload capacity of 47.92 bits per pixel, and at the same time, it can effectively avoid the detection of steganalysis tools while keeping the stego-image undistorted. Meanwhile, StegaoCNN has good generalization capabilities and can realize the steganography of different data types, such as remote sensing images and aerial images.

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