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.

Quantum steganography based on reflected gray code for color images

2020 ◽  
Vol 14 (3) ◽  
pp. 291-312
Author(s):  
Hong Xiao ◽  
Panchi Li
Keyword(s):  
Gray Code ◽  
Image Steganography ◽  
Cover Image ◽  
Embedding Capacity ◽  
Quantum Steganography ◽  
Stego Image ◽  
Quantum Image ◽  
Image Pixels ◽  
Quantum Image Steganography ◽  
Digital Steganography

Digital steganography is the art and science of hiding information in covert channels, so as to conceal the information and prevent the detection of hidden messages. On the classic computer, the principle and method of digital steganography has been widely and deeply studied, and has been initially extended to the field of quantum computing. Quantum image steganography is a relatively active branch of quantum image processing, and the main strategy currently used is to modify the LSB of the cover image pixels. For the existing LSB-based quantum image steganography schemes, the embedding capacity is no more than 3 bits per pixel. Therefore, it is meaningful to study how to improve the embedding capacity of quantum image steganography. This work presents a novel steganography using reflected Gray code for color quantum images, and the embedding capacity of this scheme is up to 6 bits per pixel. In proposed scheme, the secret qubit sequence is considered as a sequence of 6-bit segments. For 6 bits in each segment, the first 3 bits are embedded into the second LSB of RGB channels of the cover image, and the remaining 3 bits are embedded into the LSB of RGB channels of the cover image using reflected-Gray code to determine the embedded bit from secret information. Following the transforming rule, the LSBs of stego-image are not always same as the secret bits and the differences are up to almost 50%. Experimental results confirm that the proposed scheme shows good performance and outperforms the previous ones currently found in the literature in terms of embedding capacity.

scholarly journals A secure cloud system for maintaining COVID-19 patient's data using image steganography

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Nandhini Subramanian ◽  
Somaya Al-Maadeed
Keyword(s):  
Deep Learning ◽  
Image Steganography ◽  
Cover Image ◽  
Main Concern ◽  
Stego Image ◽  
Sensitive Data ◽  
Privacy And Security ◽  
Private Patient ◽  
Hiding Capacity ◽  
Smart Healthcare

Background: The COVID-19 pandemic has been life-threatening for many people and as such, a contactless medical system is necessary to prevent the spread of the virus. Smart healthcare systems collect data from patients at one end and process the acquired data at the other end. The cloud is the central point and the communication happens through insecure channels. The main concern, in this case, is the violation of privacy and security as the channel is untrusted. Traditional methods do not provide enough hiding capacity, security, and robustness. This work proposes an image steganography method using the deep learning method to hide the patient's medical images inside an innocent cover image in such a way that they are not visible to human eyes which reduces the suspicions of the presence of sensitive data. Methods: An auto encoder-decoder-based model is proposed with three components: the pre-processing module, the embedding network, and the extraction network. Features from the cover image and the secret images are extracted and fused to reconstruct the stego image. The stego image is then used to extract the ingrained secret image.shows the overall system workflow. Results: Peak Signal-to-Noise Ratio (PSNR) is the evaluation metrics used. The ImageNet dataset was used for training and testing the proposed model.shows the image results of the proposed method. Conclusion: During a COVID-19 screening test, private patient data such as mobile number and Qatari identity card are collected, transferred, and stored through untrusted channels. It is of paramount importance to preserve the privacy, security, and confidentiality of the collected patient records. A secure deep learning-based image steganography method is proposed to secure the sensitive data transferred through untrusted channels in a cloud-based system.

scholarly journals Reference-free differential histogram-correlative detection of steganography: performance analysis

2022 ◽  
Vol 25 (1) ◽  
pp. 329
Author(s):  
Natiq M. Abdali ◽  
Zahir M. Hussain
Keyword(s):  
Neural Networks ◽  
Performance Analysis ◽  
Recent Work ◽  
Detection Method ◽  
Spatial Domain ◽  
Image Steganography ◽  
Least Significant Bit ◽  
Stego Image ◽  
Image Tampering ◽  
Derivatives Of

<span lang="EN-US">Recent <span>research has demonstrated the effectiveness of utilizing neural networks for detect tampering in images. However, because accessing a database is complex, which is needed in the classification process to detect tampering, reference-free steganalysis attracted attention. In recent work, an approach for least significant bit (LSB) steganalysis has been presented based on analyzing the derivatives of the histogram correlation. In this paper, we further examine this strategy for other steganographic methods. Detecting image tampering in the spatial domain, such as image steganography. It is found that the above approach could be applied successfully to other kinds of steganography with different orders of histogram-correlation derivatives. Also, the limits of the ratio stego-image to cover are considered, where very small ratios can escape this detection method unless </span> modified.</span>

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

scholarly journals A Compression Resistant Steganography Based on Differential Manchester Code

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 165
Author(s):  
Shuai Wang ◽  
Ning Zheng ◽  
Ming Xu
Keyword(s):  
Jpeg Compression ◽  
Image Steganography ◽  
Stego Image ◽  
Practical Applications ◽  
Prior Art ◽  
The Social ◽  
Manchester Code ◽  
Before And After ◽  
Dct Coefficients ◽  
Embedding Distortion

In the field of image steganography research, more and more attention is paid to the importance of stego image robustness. In order to make steganography accessible from laboratory to practical applications, it becomes critical that the stego images can resist JPEG compression from transmission channel. In this paper, an image steganography algorithm with strong robustness to resist JPEG compression is proposed. First, the robust cover elements are selected using the sign of DCT coefficients which are kept constant before and after JPEG compression. Additionally, a distortion function and a weighted cost adjustment method are designed to assign an appropriate cost to each candidate DCT coefficient. Finally, the message is embedded in the cover image which has minimal embedding distortion by flipping the signs of DCT coefficients, while differential Manchester code is applied to the element positions to obtain the location feature. Compared with the prior art, our algorithm has better undetectability and stronger robustness, and it can resist the attacks from the social network platforms such as Facebook, Twitter, and WeChat.

scholarly journals Text embedding using image steganography in spatial domain

2018 ◽  
Vol 7 (3.6) ◽  
pp. 1 ◽  
Author(s):  
P Srilakshmi ◽  
Ch Himabindu ◽  
N Chaitanya ◽  
S V. Muralidhar ◽  
M V. Sumanth ◽  
...  
Keyword(s):  
Text Message ◽  
Spatial Domain ◽  
Image Steganography ◽  
Text Data ◽  
Novel Approach

This paper proposed novel approach of image steganography for text embedding in spatial domain. In the proposed embedding the message is dumped into the image with reference to a random generated key, based on this key the extraction of text is done from the image. So this method is a highly secured from eavesdropping and highly complex to identify the text data in the image and retrieving the text message from the message is also a resilient process. The extraction is only possible when the key is known. 

Enhancing the confidentiality of text embedding using image steganography in spatial domain

2021 ◽  
Author(s):  
N. Karthikeyan ◽  
K. Kousalya ◽  
N. Krishnamoorthy ◽  
N. Jayapandian
Keyword(s):  
Spatial Domain ◽  
Image Steganography

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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