scholarly journals Design and Development of Semi-Blind Image Steganography Algorithm using DCT for Securing Text Documents which Resist against JPEG Compression

2019 ◽  
Vol 8 (6) ◽  
pp. 4828-4832
Keyword(s):  
Jpeg Compression ◽  
Block Matrix ◽  
Image Steganography ◽  
Binary Representation ◽  
Secret Data ◽  
Stego Image ◽  
Text Documents ◽  
Algorithm Development ◽  
Dc Coefficient ◽  
Blind Image

Semi-blind Image Steganography algorithm development proposed by using DC coefficients of DCT technique. Create KEY vector and potential block matrix while embedding the secret data. Embed one secret character in one DCT block using the DC value of each block. Convert DC coefficient to binary representation and store positions for secret data. Apply JPEG compression on Stego Image. While extracting the secret data from compressed Stego Image, with the use of a KEY vector extracts secret data bits from potential blocks. After creating simulation, perform some test on a standard dataset and compare the results with target results

Image Steganography Using DWT and Semi Hexadecimal Code Based on PSNR

2018 ◽  
Vol 6 (8) ◽  
pp. 230
Author(s):  
Balkar Singh
Keyword(s):  
Visual Quality ◽  
Threshold Value ◽  
Image Steganography ◽  
Cover Image ◽  
Discrete Wavelet ◽  
Secret Data ◽  
Stego Image ◽  
Pixel Value ◽  
Better Than

In this paper, a novel image steganography approach is proposed to enhance the visual quality of stego image. The cover image is decomposed using Discrete Wavelet Transform (DWT) to produce wavelet subbands and threshold value is calculated for each higher frequency wavelet subbands. Wavelet coefficients having magnitude larger than the threshold of its subband are selected to embed the secret data. Semi Hexadecimal Code (SHC) is proposed to convert pixel value of secret image into smaller equivalent value so that it distorts stego image as less as possible. Experimental results shows that maximum PSNR between cover image and stego image is more than 75 dB .Proposed approach is also compared with the existing approaches and this comparison shows that the proposed approach is better than the existing approaches. 

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 Multi-Edge Concept used for Image Steganography

2019 ◽  
Vol 7 (1) ◽  
pp. 1-12
Author(s):  
Rasber Dh. Rashid ◽  
Ladeh S. Abdulrahman ◽  
Taban F. Majeed
Keyword(s):  
Spatial Domain ◽  
Jpeg Compression ◽  
Image Steganography ◽  
Stego Image ◽  
Sensitive Data ◽  
Digital Steganography

Digital Steganography means hiding sensitive data inside a cover object ina way that is invisible to un-authorized persons. Many proposed steganography techniques in spatial domain may achieve high invisibility requirement but sacrifice the good robustness against attacks. In some cases, weneed to take in account not just the invisibility but also we need to thinkabout other requirement which is the robustness of recovering the embedded secrete messages. In this paper we propose a new steganoraphicscheme that aims to achieve the robustness even the stego image attackedby steganalyzers. Furthermore, we proposed a scheme which is more robust against JPEG compression attack compared with other traditionalsteganography schemes.

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 Authentic Data Transmission in Image with Different LSB Methods

2019 ◽  
Vol 8 (6) ◽  
pp. 4625-4628
Keyword(s):  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Third Party ◽  
Secret Message ◽  
Secret Key ◽  
Secret Data ◽  
Stego Image ◽  
Audio Data ◽  
Carrier Image ◽  
Authentic Data

A technique to hide undisclosed information from third party as well, the method of investigation to conceal secret data into the cover frame like text, audio, image and video without any change in substantial results to the carrier image is nothing but Steganography. The contemporary safe and taut steganography of image represents an exigent form of transformation of the inserted secrecy for the receiver with getting undetected [1-5]. In Image steganography, image is the carrier and any secret message (audio or text or image) can be transmitted. This algorithm of LSB can be executed in embedding territory where the secret audio data is inserted into the LSB of envelope image for creating the stego image. This paper gives the hiding of audio data as secret data in an image file using LSB with secret key and an improved inverted LSB image Steganography with improved mean square error and peak signal to noise ratio.

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 A High-Capacity Image Steganography Method Using Chaotic Particle Swarm Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Aya Jaradat ◽  
Eyad Taqieddin ◽  
Moad Mowafi
Keyword(s):  
Particle Swarm Optimization ◽  
Image Quality ◽  
High Capacity ◽  
Particle Swarm ◽  
Image Steganography ◽  
Embedding Capacity ◽  
Secret Data ◽  
Stego Image ◽  
Swarm Optimization ◽  
Chaotic Particle Swarm Optimization

Image steganography has been widely adopted to protect confidential data. Researchers have been seeking to improve the steganographic techniques in order to increase the embedding capacity while preserving the stego-image quality. In this paper, we propose a steganography method using particle swarm optimization and chaos theory aiming at finding the best pixel locations in the cover image to hide the secret data while maintaining the quality of the resultant stego-image. To enhance the embedding capacity, the host and secret images are divided into blocks and each block stores an appropriate amount of secret bits. Experimental results show that the proposed scheme outperforms existing methods in terms of the PSNR and SSIM image quality metrics.

High Capacity Steganographic Scheme for JPEG Compression Using Particle Swarm Optimization

2012 ◽  
Vol 433-440 ◽  
pp. 5118-5122
Author(s):  
Feno Heriniaina Rabevohitra ◽  
Jun Sang
Keyword(s):  
Particle Swarm Optimization ◽  
High Capacity ◽  
Particle Swarm ◽  
Jpeg Compression ◽  
Substitution Matrix ◽  
Secret Data ◽  
Stego Image ◽  
Swarm Optimization ◽  
Best Fit

A steganographic scheme for JPEG compressed image with high capacity and with good quality of the stego-image was presented. A quantization table of size 16*16 was used instead of the commonly used size 8*8 in most JPEG compression to obtain higher embedding capacity. In addition, to improve the quality of the stego-image, particle swarm optimization (PSO) was applied to find an optimal substitution matrix to transform the secret data into the best fit for the cover image before embedding. The experimental results show that, for the proposed scheme, the improvement of the quality of the stego-image and a higher capacity of the secret data was achieved.

scholarly journals Application of Genetic Algorithm and Particle Swarm Optimization techniques for improved image steganography systems

Open Physics ◽  
2016 ◽  
Vol 14 (1) ◽  
pp. 452-462 ◽  
Author(s):  
Duraisamy Jude Hemanth ◽  
Subramaniyan Umamaheswari ◽  
Daniela Elena Popescu ◽  
Antoanela Naaji
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Frequency Domain ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Image Steganography ◽  
Secret Data ◽  
Stego Image ◽  
Swarm Optimization ◽  
Transform Coefficients

AbstractImage steganography is one of the ever growing computational approaches which has found its application in many fields. The frequency domain techniques are highly preferred for image steganography applications. However, there are significant drawbacks associated with these techniques. In transform based approaches, the secret data is embedded in random manner in the transform coefficients of the cover image. These transform coefficients may not be optimal in terms of the stego image quality and embedding capacity. In this work, the application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been explored in the context of determining the optimal coefficients in these transforms. Frequency domain transforms such as Bandelet Transform (BT) and Finite Ridgelet Transform (FRIT) are used in combination with GA and PSO to improve the efficiency of the image steganography system.

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