Encoder-Decoder Architecture for Image Steganography using Skip Connections.

Kumar, Anil; Rani, Rajneesh; Singh, Samayveer

doi:10.1016/j.procs.2023.01.091

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…With this method, secrets could be kept hidden in the area in a way that worked with the environment. This is what Wang et al [30] showed when they were working on steganography: a DS system built on a capsule network that can send extra data. Akshay Kumara et.al.…”

Section: Image Steganographymentioning

confidence: 82%

Comparative Performance Analysis Of Deep Learning-Based Image Steganography Using U-Net, V-Net, And U-Net++ Encoders

Kaneria,

Jotwani

2024

jaz

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Digital Imaging steganography is the act of hiding information in a cover picture in a way that can't be found or recovered. Three main types of methods are used in digital image steganography: neural network methods, spatial methods, and transform methods. The pixel values of an image are changed by spatial methods to embed information. On the other hand, the frequency of the image is changed by transform methods to embed information that is hidden. There are methods that use neural networks to hide things, and this is what the suggested method is all about. Through digital image steganography, this study looks into how deep convolutional neural networks (CNNs) can be used. With the increasing concerns about data infringement during transmission and storage, image steganography techniques have gained attention for hiding secret information within cover images. Traditional methods suffer from limitations such as low embedding capacity and poor reconstruction quality. To address these challenges, deep learning-based approaches have been proposed in the literature. Among them, the Convolutional Neural Network (CNN) based U-Net encoder has been extensively studied. However, its comparative performance with other CNN-based encoders like V-Net and U-Net++ remains unexplored in the context of image steganography. In this paper, we implement V-Net and U-Net++ encoders for image steganography and conduct a comprehensive performance assessment alongside U-Net architecture. These architectures are utilized to conceal a secret image within a cover image, and a unified and robust decoder is designed to extract the hidden information. Through experimental evaluations, we compare the embedding capacity, stego quality, and reconstruction quality of the three architectures. The U-Net architecture outperforms V-Net and U-Net++ in terms of embedding capacity and the quality of stego and reconstructed secret images. This research provides valuable insights into the effectiveness of different deep learning-based encoders for image steganography applications, aiding in the selection of appropriate architectures for securing digital images against unauthorized access.

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Section: Image Steganographymentioning

confidence: 82%

Comparative Performance Analysis Of Deep Learning-Based Image Steganography Using U-Net, V-Net, And U-Net++ Encoders

Kaneria,

Jotwani

2024

jaz

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“…However, the PSNR of 48.42 indicates some loss in image quality compared to the previous method. Akshay 30 and his collaborators employ deep learning on images, achieving an impressive embedding capacity of 24 Bpp. While the exact payload is not specified, this approach prioritizes data capacity over PSNR, which is lower at 37.55, indicating some visual quality degradation.…”

Section: Methodsmentioning

confidence: 99%

High-capacity data hiding for medical images based on the mask-RCNN model

Saidi,

Tibermacine,

Elhadad

2024

Sci Rep

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This study introduces a novel approach for integrating sensitive patient information within medical images with minimal impact on their diagnostic quality. Utilizing the mask region-based convolutional neural network for identifying regions of minimal medical significance, the method embeds information using discrete cosine transform-based steganography. The focus is on embedding within “insignificant areas”, determined by deep learning models, to ensure image quality and confidentiality are maintained. The methodology comprises three main steps: neural network training for area identification, an embedding process for data concealment, and an extraction process for retrieving embedded information. Experimental evaluations on the CHAOS dataset demonstrate the method’s effectiveness, with the model achieving an average intersection over union score of 0.9146, indicating accurate segmentation. Imperceptibility metrics, including peak signal-to-noise ratio, were employed to assess the quality of stego images, with results showing high capacity embedding with minimal distortion. Furthermore, the embedding capacity and payload analysis reveal the method’s high capacity for data concealment. The proposed method outperforms existing techniques by offering superior image quality, as evidenced by higher peak signal-to-noise ratio values, and efficient concealment capacity, making it a promising solution for secure medical image handling.

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“…In our analysis, the following parameters implemented for qualitative evaluation, are the mean squared error (MSE) and peak signal-to-noise ratio (PSNR), which are defined by the equations [58]:…”

Section: Mean Squared Error Peak Signal-to-noise Ratio and Structural...mentioning

confidence: 99%

Preliminary Experiments of a Real-World Authentication Mechanism Based on Facial Recognition and Fully Homomorphic Encryption

Crihan,

Dumitriu,

Crăciun

2024

Applied Sciences

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In the current context in which user authentication is the first line of defense against emerging attacks and can be considered a defining element of any security infrastructure, the need to adopt alternative, non-invasive, contactless, and scalable authentication mechanisms is mandatory. This paper presents initial research on the design, implementation, and evaluation of a multi-factor authentication mechanism that combines facial recognition with a fully homomorphic encryption algorithm. The goal is to minimize the risk of unauthorized access and uphold user confidentiality and integrity. The proposed device is implemented on the latest version of the Raspberry Pi and Arduino ESP 32 modules, which are wirelessly connected to the computer system. Additionally, a comprehensive evaluation, utilizing various statistical parameters, demonstrates the performance, the limitations of the encryption algorithms proposed to secure the biometric database, and also the security implications over the system resources. The research results illustrate that the Brakerski–Gentry–Vaikuntanathan algorithm can achieve higher performance and efficiency when compared to the Brakerski–Fan–Vercauteren algorithm, and proved to be the best alternative for the designed mechanism because it effectively enhances the level of security in computer systems, showing promise for deployment and seamless integration into real-world scenarios of network architectures.

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Encoder-Decoder Architecture for Image Steganography using Skip Connections.

Cited by 7 publications

References 7 publications

Comparative Performance Analysis Of Deep Learning-Based Image Steganography Using U-Net, V-Net, And U-Net++ Encoders

Comparative Performance Analysis Of Deep Learning-Based Image Steganography Using U-Net, V-Net, And U-Net++ Encoders

High-capacity data hiding for medical images based on the mask-RCNN model

Preliminary Experiments of a Real-World Authentication Mechanism Based on Facial Recognition and Fully Homomorphic Encryption

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