scholarly journals Scrambling Based Riffle Shift on Stego-Image to Channelize the Ensured Data

2022 ◽  
Vol 32 (1) ◽  
pp. 221-235
Author(s):  
R. Bala Krishnan ◽  
M. M. Anishin Raj ◽  
N. Rajesh Kumar ◽  
B. Karthikeyan ◽  
G. Manikandan ◽  
...  
Keyword(s):  
Stego Image

scholarly journals File Concealment using the Paillier Method and RGB Intensity Based Steganography

2018 ◽  
Author(s):  
Andysah Putera Utama Siahaan ◽  
Solly Aryza
Keyword(s):  
Large Scale ◽  
Residue Class ◽  
Public Key Cryptography ◽  
Extraction Process ◽  
Color Intensity ◽  
Stego Image ◽  
Large Scale Data ◽  
Paillier Cryptosystem ◽  
Scale Data ◽  
Rgb Image

Steganography is related to the addition of information to a given medium (referred to as cover media) without making visible changes to it. Most of the proposed steganography techniques cannot be applied to store large-scale data. In the new technique for RGB image steganography, color intensity (R-G-B) is used to determine the number of bits you want to store in each pixel. Meanwhile, to improve the security of stored confidential files, cryptographic methods will be applied. The Paillier cryptosystem invented by Pascal Paillier in 1999 is a probabilistic asymmetric algorithm for public key cryptography. The security of the Paillier algorithm depends on the problem of calculating the n-residue class that is believed to be very difficult to compute. This problem is known as the Composite Residuosity (CR) and is the basis of this Paillier cryptosystem. The software created can save secret files into a digital image into a stego image. The secret file can be extracted out through the extraction process.

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 Encryption System for Hiding Information Based on Internet of Things

2021 ◽  
Vol 15 (02) ◽  
pp. 172
Author(s):  
Hala A. Naman ◽  
Naseer Ali Hussien ◽  
Mohand Lokman Al-dabag ◽  
Haider Th.Salim Alrikabi
Keyword(s):  
World War Ii ◽  
Internet Of Things ◽  
Data Storage ◽  
Technological Development ◽  
Subjective Evaluation ◽  
Primary Objective ◽  
The Internet ◽  
Secret Key ◽  
Stego Image ◽  
The Internet Of Things

<p class="0abstract">One of the unexpected intelligence tactics known in World War II was to conceal the data in images that were reduced to the size of a point that was used in every text and transported in front of the enemy's eyes. In the new age, and after the expansion of Internet science and the use of the Internet worldwide, we will establish a security feature of the IOT service that will work more reliably and more effectively to deal with the Internet of Things and ensure the work of the services that the customer interacts with. A secret-key stenographic scheme that embeds four gray-scale secret size (128*128) pixel images into a size (512*512) pixel cover image in this work. Wavelet transform is the method used in this project to analyze the cover into its frequency components. In this work, combinations of steganography and cryptography were made to increase the level of safety and make the device more difficult for attackers to beat. The resulting stego-image that will be transmitted did not raise any suspicion by both objective and subjective evaluation, so the primary objective of Steganography is achieved. The proposed system was designed by using (MATLAB R2018b) and running on a Pentium-4 computer. The Internet of Things works with the encryption system for data in a synchronized manner with the technological development, and in order to maintain the stability of any Internet of things service, whether it is information signal services, visual or audio data, a remote control system, or data storage in the Internet cloud, we must focus on data preservation from internet pirates and internet system hackers. The picture Figure<strong> </strong>4 below shows the method of encryption and dealing with the Internet of things system..</p>

scholarly journals An Improved Reversible Image Transformation Using K-Means Clustering and Block Patching

Information ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 17 ◽  
Author(s):  
Haidong Zhong ◽  
Xianyi Chen ◽  
Qinglong Tian
Keyword(s):  
Image Quality ◽  
Data Hiding ◽  
Privacy Protection ◽  
Reversible Data Hiding ◽  
Auxiliary Information ◽  
Experimental Results ◽  
Image Transformation ◽  
Clustering Method ◽  
Stego Image ◽  
Encrypted Images

Recently, reversible image transformation (RIT) technology has attracted considerable attention because it is able not only to generate stego-images that look similar to target images of the same size, but also to recover the secret image losslessly. Therefore, it is very useful in image privacy protection and reversible data hiding in encrypted images. However, the amount of accessorial information, for recording the transformation parameters, is very large in the traditional RIT method, which results in an abrupt degradation of the stego-image quality. In this paper, an improved RIT method for reducing the auxiliary information is proposed. Firstly, we divide secret and target images into non-overlapping blocks, and classify these blocks into K classes by using the K-means clustering method. Secondly, we match blocks in the last (K-T)-classes using the traditional RIT method for a threshold T, in which the secret and target blocks are paired with the same compound index. Thirdly, the accessorial information (AI) produced by the matching can be represented as a secret segment, and the secret segment can be hided by patching blocks in the first T-classes. Experimental results show that the proposed strategy can reduce the AI and improve the stego-image quality effectively.

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.

scholarly journals An Advanced Reversible Data Hiding Algorithm Using Local Similarity, Curved Surface Characteristics, and Edge Characteristics in Images

2020 ◽  
Vol 10 (3) ◽  
pp. 836 ◽  
Author(s):  
Soo-Mok Jung ◽  
Byung-Won On
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Image Watermarking ◽  
Surface Characteristics ◽  
Cover Image ◽  
Curved Surface ◽  
Local Similarity ◽  
Stego Image ◽  
Confidential Data ◽  
The Difference

In this paper, we proposed methods to accurately predict pixel values by effectively using local similarity, curved surface characteristics, and edge characteristics present in an image. Furthermore, to hide more confidential data in a cover image using the prediction image composed of precisely predicted pixel values, we proposed an effective data hiding technique that applied the prediction image to the conventional reversible data hiding technique. Precise prediction of pixel values greatly increases the frequency at the peak point in the histogram of the difference sequence generated using the cover and prediction images. This considerably increases the amount of confidential data that can be hidden in the cover image. The proposed reversible data hiding algorithm (ARDHA) can hide up to 24.5% more confidential data than the existing algorithm. Moreover, it is not possible to determine the presence of hidden confidential data in stego-images, as they possess excellent visual quality. The confidential data can be extracted from the stego-image without loss, and the original cover image can be restored from the stego-image without distortion. Therefore, the proposed algorithm can be effectively used in digital image watermarking, military, and medical applications.

A Novel Calibration Used for Blind Steganalysis of JPEG Images

2014 ◽  
Vol 971-973 ◽  
pp. 1504-1507
Author(s):  
Ling Wang ◽  
Jing Xin Hong ◽  
Xu Chen ◽  
Qian Chen ◽  
Yi Xiong Zhang
Keyword(s):  
High Accuracy ◽  
Support Vector ◽  
Specific Function ◽  
Calibration Technique ◽  
Stego Image ◽  
Jpeg Images ◽  
Class Separation ◽  
The Difference ◽  
Nonlinear Classifier ◽  
Blind Steganalysis

In this paper, we present a new calibration technique aimed at blind steganalysis for JPEG images, which can magnify the difference between cover images and stego images. The calibration can be considered as a preprocessing of stego image before extracting the features. So the calibrated features are calculated as the difference between a specific function calculated from the original stego image and the same function obtained from calibrated version. Moreover, the calibrated feature was used to train SVM (support vector machine), a nonlinear classifier, which is effective in class separation. For comparison, a database composed of 6690 cover and stego images (generated by using four different embedding schemes) was established. Based on this database, we conducted extensive experiments and drawn a conclusion that the steganalysis based on our novel calibration can detect the stego images with high accuracy.

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