A convolutional neural network model based reversible data hiding scheme in encrypted images with block-wise Arnold transform

Panchikkil, Shaiju; Manikandan, V. M.; Zhang, Yudong

doi:10.1016/j.ijleo.2021.168137

Cited by 13 publications

(19 citation statements)

References 34 publications

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“…A comparison of the methodology used by various similar RDH schemes and the proposed scheme is shown in Table 10 . All the schemes that have been compared, are the schemes where the extraction of the embedded additional information is separated from the image recovery except [ 33 ] and [ 35 ]. The bit error generated during the extraction is 0 in all the schemes.…”

Section: Comparison With Other Rdh Schemesmentioning

confidence: 99%

“…The RDH scheme in [ 33 ] used Arnold transform scrambling technique to hide the private data. The image is processed as blocks of size M × M and is transformed into different matrices using the Arnold transform algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Here the authors have utilized the cyclic property of Arnold transform to hide the private data. To reduce the overhead of transferring the machine learning model with the receiver in [ 33 ], the recovery of blocks of the cover image is facilitated through the correlation strength of the pixels in [ 34 ]. Whereas the scheme in [ 7 ] trained a linear regression-based predictor for RDH.…”

Section: Introductionmentioning

confidence: 99%

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A prediction error based reversible data hiding scheme in encrypted image using block marking and cover image pre-processing

Panchikkil

Manikandan

2023

Multimed Tools Appl

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A drastic change in communication is happening with digitization. Technological advancements will escalate its pace further. The human health care systems have improved with technology, remodeling the traditional way of treatments. There has been a peak increase in the rate of telehealth and e-health care services during the coronavirus disease 2019 (COVID-19) pandemic. These implications make reversible data hiding (RDH) a hot topic in research, especially for medical image transmission. Recovering the transmitted medical image (MI) at the receiver side is challenging, as an incorrect MI can lead to the wrong diagnosis. Hence, in this paper, we propose a MSB prediction error-based RDH scheme in an encrypted image with high embedding capacity, which recovers the original image with a peak signal-to-noise ratio (PSNR) of dB and structural similarity index (SSIM) value of 1. We scan the MI from the first pixel on the top left corner using the snake scan approach in dual modes: i) performing a rightward direction scan and ii) performing a downward direction scan to identify the best optimal embedding rate for an image. Banking upon the prediction error strategy, multiple MSBs are utilized for embedding the encrypted PHR data. The experimental studies on test images project a high embedding rate with more than 3 bpp for 16-bit high-quality DICOM images and more than 1 bpp for most natural images. The outcomes are much more promising compared to other similar state-of-the-art RDH methods.

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Section: Comparison With Other Rdh Schemesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A prediction error based reversible data hiding scheme in encrypted image using block marking and cover image pre-processing

Panchikkil

Manikandan

2023

Multimed Tools Appl

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“…Machine learning models are also used in RDH. In [ 32 ], a convolutional neural network (CNN) framework supports recovering the cover image and hence the embedded data from the marked image. Here, the data embedding is made possible through an image-scrambling algorithm called the Arnold transform algorithm.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Directional Pixel-Swapping Approach (MPSA) for Entropy-Retained Reversible Data Hiding in Encrypted Images

Panchikkil

Manikandan

Zhang

et al. 2023

Entropy

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Reversible data hiding (RDH), a promising data-hiding technique, is widely examined in domains such as medical image transmission, satellite image transmission, crime investigation, cloud computing, etc. None of the existing RDH schemes addresses a solution from a real-time aspect. A good compromise between the information embedding rate and computational time makes the scheme suitable for real-time applications. As a solution, we propose a novel RDH scheme that recovers the original image by retaining its quality and extracting the hidden data. Here, the cover image gets encrypted using a stream cipher and is partitioned into non-overlapping blocks. Secret information is inserted into the encrypted blocks of the cover image via a controlled local pixel-swapping approach to achieve a comparatively good payload. The new scheme MPSA allows the data hider to hide two bits in every encrypted block. The existing reversible data-hiding schemes modify the encrypted image pixels leading to a compromise in image security. However, the proposed work complements the support of encrypted image security by maintaining the same entropy of the encrypted image in spite of hiding the data. Experimental results illustrate the competency of the proposed work accounting for various parameters, including embedding rate and computational time.

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“…Panchikkil et al introduce a reversible data hiding scheme in encrypted images that incorporates a convolutional neural network (CNN) model. This work explores the ability of CNNs as a classification model to separate encrypted blocks from natural blocks [35]. The above studies show that neural networks are very suitable for image encryption.…”

Section: Introductionmentioning

confidence: 99%

Bit‐level image encryption algorithm based on fully‐connected‐like network and random modification of edge pixels

Sheng

et al. 2022

IET Image Processing

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A bit-level image encryption algorithm based on Fully-Connected-Like network(FCLN) and random modification of edge pixels is proposed. In the paper, in order to enhance the security of the cryptographic system, random noise is first used to modify the least significant bits of the edge pixels of the image, and the modified image is used as the input image. Later,the chaotic sequence is used to perform cyclic shift transformation on the image. In the subsequent steps, the FCLN is generated based on a fully connected neural network, which can perform scrambling and diffusion operations on the input image. Finally, the bidirectional diffusion method is used to diffuse the image forward and backward. In addition, the image after the edge pixel modification is convolved with the chaotic sequence, and the initial value of the chaotic system is set by the result to establish the correlation between the plain image and the algorithm, which makes the algorithm resistant to known/chosen plaintext attack. Experimental results show that although the image is modified by random noise, the decrypted image is visually the same as the original image. At the same time, through the analysis of common attacks such as differential attacks, noise attacks, and data loss attacks, our algorithm shows high security.

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A convolutional neural network model based reversible data hiding scheme in encrypted images with block-wise Arnold transform

Cited by 13 publications

References 34 publications

A prediction error based reversible data hiding scheme in encrypted image using block marking and cover image pre-processing

A prediction error based reversible data hiding scheme in encrypted image using block marking and cover image pre-processing

A Multi-Directional Pixel-Swapping Approach (MPSA) for Entropy-Retained Reversible Data Hiding in Encrypted Images

Bit‐level image encryption algorithm based on fully‐connected‐like network and random modification of edge pixels

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