scholarly journals Improving the efficiency of the steganographic method of data hiding with the application of iterative functions and noise addition

2021 ◽  
Vol 3 (2) ◽  
pp. 66-73
Author(s):  
I. M. Zhuravel ◽  
◽  
L. Z. Mychuda ◽  
Yu. I. Zhuravel ◽  
◽  
...  
Keyword(s):  
Data Hiding ◽  
Communication Channels ◽  
Visual Quality ◽  
Performance Criterion ◽  
Secret Key ◽  
Open Communication ◽  
Common Criteria ◽  
Secure Data Transmission ◽  
Hidden Data ◽  
Iterative Functions

The development of computer and digital technology contributes to the growth of information flows transmitted through open and closed communication channels. In many cases, this information is confidential, financial, or commercial in nature and is of value to its owners. This requires the development of mechanisms to protect information from unauthorized access. There are two fundamental areas of secure data transmission over the open communication channels – cryptography and steganography. The fundamental difference between them is that cryptography hides from others the content of the message, and steganography hides the very fact of the message transmission. This paper is devoted to steganographic methods of data concealment, which are less researched than cryptographic, but have significant potential for use in a variety of applications. One of the important characteristics of most methods is their effectiveness. In general, efficiency is assessed in the context of solving specific problems. However, the most common criteria for the effectiveness of steganographic methods are the amount of hidden data and the method of transmitting the secret key to the receiving party, which will not allow the attacker to intercept it. Because media files make up a significant portion of network traffic, a digital image is chosen as the stegocontainer. It is proposed to determine the coordinates of the embedding location on the basis of iterative functions. The advantage of their use is the compactness of the description of the coordinates of the pixels in which the data will be hidden. In addition, it is proposed to use the Diffie-Gellman algorithm to transfer the parameters of iterative functions to the receiving side. This method of key distribution makes the steganographic method less vulnerable to being stolen by an attacker. The second performance criterion is the amount of hidden data. The paper found that the moderate addition of multiplicative noise makes it possible to increase the amount of hidden data without significantly reducing the visual quality of the stegocontainer. To analyze the distortions in the image-stegocontainer, which are due to the influence of noise and modification of the lower bits of pixels, the method of a quantitative assessment of visual quality is used, which is based on the laws of visual perception. Keywords: steganographic data hiding; hiding efficiency; iterative functions; Diffie-Gelman algorithm.

Fuzzy Proximity based Robust Data Hiding Scheme with Interval Threshold

2022 ◽  
Author(s):  
Prabhas Kumar Singh ◽  
Biswapati Jana ◽  
Kakali Datta
Keyword(s):  
Fuzzy Logic ◽  
Data Hiding ◽  
Processing System ◽  
Visual Quality ◽  
Structural Similarity ◽  
Secret Message ◽  
Secret Key ◽  
Post Processing ◽  
Coincidence Problem ◽  
Shared Secret

Abstract In 2020, Ashraf et al. proposed an interval type-2 fuzzy logic based block similarity calculation using color proximity relations of neighboring pixels in a steganographic scheme. Their method works well for detecting similarity, but it has drawbacks in terms of visual quality, imperceptibility, security, and robustness. Using Mamdani fuzzy logic to identify color proximity at the block level, as well as a shared secret key and post-processing system, this paper attempts to develop a robust data hiding scheme with similarity measure to ensure good visual quality, robustness, imperceptibility, and enhance the security. Further, the block color proximity is graded using an interval threshold. Accordingly, data embedding is processed in the sequence generated by the shared secret keys. In order to increase the quality and accuracy of the recovered secret message, the tampering coincidence problem is solved through a post-processing approach. The experimental analysis, steganalysis and comparisons clearly illustrate the effectiveness of the proposed scheme in terms of visual quality, structural similarity, recoverability and robustness.

Building matrixes of higher order to achieve the special commutative multiplication and its applications in cryptography: بناء مصفوفات تبديلية من مراتب عليا وتطبيقاتها في التشفير

2021 ◽  
Vol 5 (3) ◽  
pp. 16-1
Author(s):  
Mechal Fheed Alslman, Nassr Aldin Ide, Ahmad Zakzak Mechal Fheed Alslman, Nassr Aldin Ide, Ahmad Zakzak
Keyword(s):  
Communication Channels ◽  
Higher Order ◽  
Public Key ◽  
Public Key Encryption ◽  
Secret Key ◽  
Open Communication ◽  
Encryption Method ◽  
Two Sides ◽  
Commutative Property ◽  
Square Matrix

In this paper, we introduce a method for building matrices that verify the commutative property of multiplication on the basis of circular matrices, as each of these matrices can be divided into four circular matrices, and we can also build matrices that verify the commutative property of multiplication from higher order and are not necessarily divided into circular matrices. Using these matrixes, we provide a way to securely exchange a secret encryption key, which is a square matrix, over open communication channels, and then use this key to exchange encrypted messages between two sides or two parties. Moreover, using these matrixes we also offer a public-key encryption method, whereby the two parties exchange encrypted messages without previously agreeing on a common secret key between them.

scholarly journals Effective Revisable Data Hiding in Encrypted Image for Protection of Image Content

2020 ◽  
Vol 9 (8) ◽  
pp. 155-160
Keyword(s):  
Data Hiding ◽  
Visual Quality ◽  
Data Encryption ◽  
Original Image ◽  
Image Content ◽  
Secret Data ◽  
Encrypted Image ◽  
Reversible Method ◽  
Small Change ◽  
Hidden Data

In this article, we propose a reversible method for hiding data, in which the original image and hidden data can be restored on the receiving side. The owner encrypts the original image using an encryption key to protect the privacy of the image content. Each block of encrypted image is added to the little secret by Hider data using the key data hiding. Data hiding process causes only a small change in each partial pixel flip block, which improves decoded image visual quality. The image can be easily decoded receiver using the key, data encryption key to hide the adaptive soft characteristic of the evaluation function along the direction of the isophote, the secret data can be extracted from a decoded image and original image recovery can be restored more successfully.

scholarly journals A Robust and Improved Visual Quality Data Hiding Method for HEVC

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 53984-53997 ◽  
Author(s):  
Yunxia Liu ◽  
Hongguo Zhao ◽  
Shuyang Liu ◽  
Cong Feng ◽  
Si Liu
Keyword(s):  
Data Hiding ◽  
Visual Quality ◽  
Quality Data

Reversible Audio Data Hiding in Spectral and Time Domains

2013 ◽  
pp. 19-41 ◽  
Author(s):  
Akira Nishimura
Keyword(s):  
Data Hiding ◽  
Time Domain ◽  
Reversible Data Hiding ◽  
Linear Prediction ◽  
Data Consistency ◽  
Time Domains ◽  
Audio Data ◽  
Hidden Data ◽  
The Time Domain ◽  
Location Map

Reversible data hiding is a technique whereby hidden data are embedded in host data in such a way that the host data consistency is perfectly preserved and the host data are restored when extracting the hidden data. This chapter introduces basic algorithms for reversible data hiding, histogram shifting, histogram expansion, and compression. This chapter also proposes and evaluates two reversible data hiding methods, i.e., hiding data in the frequency-domain using integer Discrete Cosine Transform (DCT) and modified DCT and hiding in the time domain using linear prediction and error expansion. As no location map is required to prevent amplitude overflow, the proposed method in the time domain achieves a storage capacity of nearly 1 bit per sample of payload data. The proposed methods are evaluated by the payload amount, objective quality degradation of stego signal, and payload concealment.

scholarly journals A Comprehensive Approach to Image Watermarking, Encryption and Steganography

2015 ◽  
Vol 8 (4) ◽  
pp. 32
Author(s):  
Sabarish Sridhar
Keyword(s):  
Data Hiding ◽  
Data Transmission ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Cover Image ◽  
Second Step ◽  
Squared Error ◽  
Feature Similarity ◽  
Secure Data Transmission

Steganography, water marking and encryption are widely used in image processing and communication. A general practice is to use them independently or in combination of two - for e.g. data hiding with encryption or steganography alone. This paper aims to combine the features of watermarking, image encryption as well as image steganography to provide reliable and secure data transmission .The basics of data hiding and encryption are explained. The first step involves inserting the required watermark on the image at the optimum bit plane. The second step is to use an RSA hash to actually encrypt the image. The final step involves obtaining a cover image and hiding the encrypted image within this cover image. A set of metrics will be used for evaluation of the effectiveness of the digital water marking. The list includes Mean Squared Error, Peak Signal to Noise Ratio and Feature Similarity.

scholarly journals Reversible Data Hiding with Pixel Prediction and Additive Homomorphism for Encrypted Image

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Chunqiang Yu ◽  
Xianquan Zhang ◽  
Zhenjun Tang ◽  
Yan Chen ◽  
Jingyu Huang
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Data Extraction ◽  
Visual Quality ◽  
Input Image ◽  
Embedding Capacity ◽  
Secret Data ◽  
Encrypted Image ◽  
High Embedding Capacity ◽  
Additive Homomorphism

Data hiding in encrypted image is a recent popular topic of data security. In this paper, we propose a reversible data hiding algorithm with pixel prediction and additive homomorphism for encrypted image. Specifically, the proposed algorithm applies pixel prediction to the input image for generating a cover image for data embedding, referred to as the preprocessed image. The preprocessed image is then encrypted by additive homomorphism. Secret data is finally embedded into the encrypted image via modular 256 addition. During secret data extraction and image recovery, addition homomorphism and pixel prediction are jointly used. Experimental results demonstrate that the proposed algorithm can accurately recover original image and reach high embedding capacity and good visual quality. Comparisons show that the proposed algorithm outperforms some recent algorithms in embedding capacity and visual quality.

scholarly journals Reversible Data Hiding Using Inter-Component Prediction in Multiview Video Plus Depth

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 514 ◽  
Author(s):  
Jin Young Lee ◽  
Cheonshik Kim ◽  
Ching-Nung Yang
Keyword(s):  
Video Compression ◽  
Data Hiding ◽  
Reversible Data Hiding ◽  
Depth Map ◽  
3D Video ◽  
Internet Technology ◽  
Secret Communication ◽  
Multiview Video ◽  
Prediction Error Expansion ◽  
Hidden Data

With the advent of 3D video compression and Internet technology, 3D videos have been deployed worldwide. Data hiding is a part of watermarking technologies and has many capabilities. In this paper, we use 3D video as a cover medium for secret communication using a reversible data hiding (RDH) technology. RDH is advantageous, because the cover image can be completely recovered after extraction of the hidden data. Recently, Chung et al. introduced RDH for depth map using prediction-error expansion (PEE) and rhombus prediction for marking of 3D videos. The performance of Chung et al.’s method is efficient, but they did not find the way for developing pixel resources to maximize data capacity. In this paper, we will improve the performance of embedding capacity using PEE, inter-component prediction, and allowable pixel ranges. Inter-component prediction utilizes a strong correlation between the texture image and the depth map in MVD. Moreover, our proposed scheme provides an ability to control the quality of depth map by a simple formula. Experimental results demonstrate that the proposed method is more efficient than the existing RDH methods in terms of capacity.

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