Image content based secure reversible data hiding scheme using block scrambling and integer wavelet transform

Meenpal, Ankita; Majumder, Saikat

doi:10.1007/s12046-022-01828-z

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…The subsequent decompositions run as:

where M is the MDL and m the current decomposition level [ 18 ].…”

Section: Methodology Of Wacpnmentioning

confidence: 99%

WACPN: A Neural Network for Pneumonia Diagnosis

Wang¹,

Khan²,

Zhu³

et al. 2023

Computer Systems Science and Engineering

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Community-acquired pneumonia (CAP) is considered a sort of pneumonia developed outside hospitals and clinics. To diagnose community-acquired pneumonia (CAP) more efficiently, we proposed a novel neural network model. We introduce the 2-dimensional wavelet entropy (2d-WE) layer and an adaptive chaotic particle swarm optimization (ACP) algorithm to train the feed-forward neural network. The ACP uses adaptive inertia weight factor (AIWF) and Rossler attractor (RA) to improve the performance of standard particle swarm optimization. The final combined model is named WE-layer ACP-based network (WACPN), which attains a sensitivity of 91.87 ± 1.37%, a specificity of 90.70 ± 1.19%, a precision of 91.01 ± 1.12%, an accuracy of 91.29 ± 1.09%, F1 score of 91.43 ± 1.09%, an MCC of 82.59 ± 2.19%, and an FMI of 91.44 ± 1.09%. The AUC of this WACPN model is 0.9577. We find that the maximum deposition level chosen as four can obtain the best result. Experiments demonstrate the effectiveness of both AIWF and RA. Finally, this proposed WACPN is efficient in diagnosing CAP and superior to six state-of-the-art models. Our model will be distributed to the cloud computing environment.

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“…The subsequent decompositions run as:

where M is the MDL and m the current decomposition level [ 18 ].…”

Section: Methodology Of Wacpnmentioning

confidence: 99%

WACPN: A Neural Network for Pneumonia Diagnosis

Wang¹,

Khan²,

Zhu³

et al. 2023

Computer Systems Science and Engineering

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“…In the year 2021, Wu et al [30] introduced a new RRBE technique that utilizes adaptive predictionerror labeling (APL) to embed encrypted data into encrypted images at specific locations, resulting in a marked encrypted image. Meenpal et al [31] introduced a secure RDH method in the year 2022, where data is embedded into low-frequency blocks of an image using the DE method within the Integer Wavelet Transform (IWT) domain. In other researchers were presented in [32]- [35] proposed secure RDH with the data encrypted are embedded into the image with Histogram Shifting method.…”

mentioning

confidence: 99%

Secure medical image watermarking based on reversible data hiding with Arnold's cat map

Arham,

Lestari

2023

Int. J. Adv. Intell. Informatics

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The process of restoring medical images to their original form after the extraction process in application watermarking is crucial for ensuring their authenticity. Inaccurate diagnoses can occur due to distortions in medical images from conventional data embedding applications. To address this issue, reversible data hiding (RDH) method has been proposed by several researchers in recent years to embed data in medical images. After the extraction process, images can be restored to their original form with a reversible data-hiding method. In the past few years, several RDH methods have been rapidly developed, which are based on the concept of difference expansion (DE). However, it is crucial to pay attention to the security of the medical image watermarking method, the embedded data with RDH method can be easily modified, accessed, and altered by unauthorized individuals if they know the employed method. This research suggests a new approach to secure the RDH method through the use of Chaotic Map-based Arnold's Cat Map algorithms on the medical images. Data embedding was performed on random medical images using a DE method. Four gray-scale medical image modalities were used to assess the proposed method's efficacy. In our approach, we can incorporate capacity up to 0.62 bpp while maintaining a visual quality up to 41.02 dB according to PSNR and 0.9900 according to SSIM. The results indicated that it can enhance the security of the RDH method while retaining the ability to embed data and preserving the visual appearance of the medical images.

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