Multi Image Steganography using Distributed LSB Algorithm and Secret Text Recovery on Stego Image Corruption

2020 ◽  
Author(s):  
Jagan Raj Jayapandiyan ◽  
C. Kavitha Kavitha ◽  
K. Sakthivel Sakthivel
Keyword(s):  
Research Work ◽  
Image Steganography ◽  
Stego Image ◽  
Secret Image ◽  
Multiple Image

In this proposed research work, an attempt has been made to use multiple image files for steganography encoding along with the capability of secret text recovery in the event of any image corruption during the transit. This algorithm is effective on the security factor of secret image since the embedded checksum will validate for any unauthorized users or intruders attempt to corrupt the picture in any aspect. If any of the stego image underwent any steganalysis or MiM attack, then this proposed algorithm can effectively regenerate the content of one stego image using other intact stego images received in the receiving end.

scholarly journals High-Capacity Image Steganography Algorithm Based on Image Style Transfer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xinliang Bi ◽  
Xiaoyuan Yang ◽  
Chao Wang ◽  
Jia Liu
Keyword(s):  
High Capacity ◽  
Image Features ◽  
Image Steganography ◽  
Cover Image ◽  
Secret Message ◽  
The Internet ◽  
Stego Image ◽  
Style Transfer ◽  
Secret Image ◽  
Secret Information

Steganography is a technique for publicly transmitting secret information through a cover. Most of the existing steganography algorithms are based on modifying the cover image, generating a stego image that is very similar to the cover image but has different pixel values, or establishing a mapping relationship between the stego image and the secret message. Attackers will discover the existence of secret communications from these modifications or differences. In order to solve this problem, we propose a steganography algorithm ISTNet based on image style transfer, which can convert a cover image into another stego image with a completely different style. We have improved the decoder so that the secret image features can be fused with style features in a variety of sizes to improve the accuracy of secret image extraction. The algorithm has the functions of image steganography and image style transfer at the same time, and the images it generates are both stego images and stylized images. Attackers will pay more attention to the style transfer side of the algorithm, but it is difficult to find the steganography side. Experiments show that our algorithm effectively increases the steganography capacity from 0.06 bpp to 8 bpp, and the generated stylized images are not significantly different from the stylized images on the Internet.

scholarly journals Spectral Graph Wavelet Based Image Steganography Using SVD and Arnold Transform

2021 ◽  
Vol 38 (4) ◽  
pp. 1113-1121
Author(s):  
Shikha Chaudhary ◽  
Saroj Hiranwal ◽  
Chandra Prakash Gupta
Keyword(s):  
Performance Metrics ◽  
Image Steganography ◽  
Sensitive Information ◽  
Stego Image ◽  
Suggested Approach ◽  
Secret Image ◽  
Spectral Graph ◽  
Vector Decomposition ◽  
Pixel Correlation

Steganography is the process of concealing sensitive information within cover medium. This study offers an efficient and safe innovative image steganography approach based on graph signal processing (GSP). To scramble the secret image, Arnold cat map transform is used, then Spectral graph wavelet is used to change the cover and scrambled secret image, followed by singular vector decomposition (SVD) of the modified cover image. To create the stego image, an alpha blending process is used. To produce the stego image, GSP-based synthesis is used. By maintaining the inter-pixel correlation, GSP improves the visual quality of the produced stego image. The effects of image processing attacks on the suggested approach are examined. The investigational results and assessment indicate that the proposed steganography scheme is more efficient and robust in terms of quality measures. The quality of stego image is evaluated in respect of PSNR, NCC, SC and AD performance metrics.

scholarly journals Stego-eHealth: An eHealth System for Secured Transfer of Medical Images using Image Steganography

2021 ◽  
Author(s):  
Nandhini Subramanian ◽  
, Jayakanth Kunhoth ◽  
Somaya Al-Maadeed ◽  
Ahmed Bouridane
Keyword(s):  
Medical Information ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Health Care Systems ◽  
Image Steganography ◽  
Cover Image ◽  
Stego Image ◽  
Hiding Capacity ◽  
Secret Image ◽  
Care Systems

COVID pandemic has necessitated the need for virtual and online health care systems to avoid contacts. The transfer of sensitive medical information including the chest and lung X-ray happens through untrusted channels making it prone to many possible attacks. This paper aims to secure the medical data of the patients using image steganography when transferring through untrusted channels. A deep learning method with three parts is proposed – preprocessing module, embedding network and the extraction network. Features from the cover image and the secret image are extracted by the preprocessing module. The merged features from the preprocessing module are used to output the stego image by the embedding network. The stego image is given as the input to the extraction network to extract the ingrained secret image. Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) are the evaluation metrics used. Higher PSNR value proves the higher security; robustness of the method and the image results show the higher imperceptibility. The hiding capacity of the proposed method is 100% since the cover image and the secret image are of the same size.

scholarly journals Multiple Image Splicing Dataset (MISD): A Dataset for Multiple Splicing

Data ◽  
2021 ◽  
Vol 6 (10) ◽  
pp. 102
Author(s):  
Kalyani Dhananjay Kadam ◽  
Swati Ahirrao ◽  
Ketan Kotecha
Keyword(s):  
Research Work ◽  
Ground Truth ◽  
Easy Access ◽  
Image Editing ◽  
Image Splicing ◽  
Misleading Information ◽  
Detection Techniques ◽  
Multiple Image ◽  
Significant Area ◽  
Social Media Platforms

Image forgery has grown in popularity due to easy access to abundant image editing software. These forged images are so devious that it is impossible to predict with the naked eye. Such images are used to spread misleading information in society with the help of various social media platforms such as Facebook, Twitter, etc. Hence, there is an urgent need for effective forgery detection techniques. In order to validate the credibility of these techniques, publically available and more credible standard datasets are required. A few datasets are available for image splicing, such as Columbia, Carvalho, and CASIA V1.0. However, these datasets are employed for the detection of image splicing. There are also a few custom datasets available such as Modified CASIA, AbhAS, which are also employed for the detection of image splicing forgeries. A study of existing datasets used for the detection of image splicing reveals that they are limited to only image splicing and do not contain multiple spliced images. This research work presents a Multiple Image Splicing Dataset, which consists of a total of 300 multiple spliced images. We are the pioneer in developing the first publicly available Multiple Image Splicing Dataset containing high-quality, annotated, realistic multiple spliced images. In addition, we are providing a ground truth mask for these images. This dataset will open up opportunities for researchers working in this significant area.

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 Reversible Multiple Image Secret Sharing Using Discrete Haar Wavelet Transform

2018 ◽  
Vol 8 (6) ◽  
pp. 5004
Author(s):  
Ashwaq T. Hashim ◽  
Suhad A. Ali
Keyword(s):  
Wavelet Transform ◽  
Haar Wavelet ◽  
Secret Image Sharing ◽  
Haar Wavelet Transform ◽  
Secret Image ◽  
Image Sharing ◽  
Multiple Image ◽  
Sharing Scheme ◽  
Image Secret Sharing ◽  
Secret Image Sharing Scheme

<p>Multiple Secret Image Sharing scheme is a protected approach to transmit more than one secret image over a communication channel. Conventionally, only single secret image is shared over a channel at a time. But as technology grew up, there is a need to share more than one secret image. A fast (r, n) multiple secret image sharing scheme based on discrete haar wavelet transform has been proposed to encrypt m secret images into n noisy images that are stored over different servers. To recover m secret images r noise images are required. Haar Discrete Wavelet Transform (DWT) is employed as reduction process of each secret image to its quarter size (i.e., LL subband). The LL subbands for all secrets have been combined in one secret that will be split later into r subblocks randomly using proposed high pseudo random generator. Finally, a developed (r, n) threshold multiple image secret sharing based one linear system has been used to generate unrelated shares. The experimental results showed that the generated shares are more secure and unrelated. The size reductions of generated shares were 1:4r of the size of each of original image. Also, the randomness test shows a good degree of randomness and security.</p>

scholarly journals A Verifiable Steganography-Based Secret Image Sharing Scheme in 5G Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Shiyue Qin ◽  
Zhenhua Tan ◽  
Fucai Zhou ◽  
Jian Xu ◽  
Zongye Zhang
Keyword(s):  
Secret Image Sharing ◽  
5G Networks ◽  
Dynamic Features ◽  
Stego Image ◽  
Collusion Attack ◽  
Secret Image ◽  
Image Sharing ◽  
Replacement Method ◽  
Sharing Scheme ◽  
Secret Image Sharing Scheme

With the development and innovation of new techniques for 5G, 5G networks can provide extremely large capacity, robust integrity, high bandwidth, and low latency for multimedia image sharing and storage. However, it will surely exacerbate the privacy problems intrinsic to image transformation. Due to the high security and reliability requirements for storing and sharing sensitive images in the 5G network environment, verifiable steganography-based secret image sharing (SIS) is attracting increasing attention. The verifiable capability is necessary to ensure the correct image reconstruction. From the literature, efficient cheating verification, lossless reconstruction, low reconstruct complexity, and high-quality stego images without pixel expansion are summarized as the primary goals of proposing an effective steganography-based SIS scheme. Compared with the traditional underlying techniques for SIS, cellular automata (CA) and matrix projection have more strengths as well as some weaknesses. In this paper, we perform a complimentary of these two techniques to propose a verifiable secret image sharing scheme, where CA is used to enhance the security of the secret image, and matrix projection is used to generate shadows with a smaller size. From the steganography perspective, instead of the traditional least significant bits replacement method, matrix encoding is used in this paper to improve the embedding efficiency and stego image quality. Therefore, we can simultaneously achieve the above goals and achieve proactive and dynamic features based on matrix projection. Such features can make the proposed SIS scheme more applicable to flexible 5G networks. Finally, the security analysis illustrates that our scheme can effectively resist the collusion attack and detect the shadow tampering over the persistent adversary. The analyses for performance and comparative demonstrate that our scheme is a better performer among the recent schemes with the perspective of functionality, visual quality, embedding ratio, and computational efficiency. Therefore, our scheme further strengthens security for the images in 5G networks.

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