Fragile high capacity data hiding in digital images using integer-to-integer DWT and lattice vector quantization

Akhtarkavan, Ehsan; Majidi, Babak; Salleh, Mohd Khairul Mohd; Patra, J.C.

doi:10.1007/s11042-020-08662-7

Cited by 12 publications

(9 citation statements)

References 49 publications

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“…The method that is provided allows for a seamless transition between the exploitation phase and the exploration phase. A delicate method for masking digital picture data that is reliant upon lattice VQ (LVQ) was provided in reference [14]. The picture was converted from the spatial domain to the frequency domain using an integer-to-integer discrete wavelet transform (DWT) during the data concealing system that was provided, as well as during a primary preprocessing phase (IIDWT).…”

Section: Related Workmentioning

confidence: 99%

Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

Kumar¹,

Srinivasan²

2023

Intelligent Automation &Amp; Soft Computing

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At the present time, the Industrial Internet of Things (IIoT) has swiftly evolved and emerged, and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data. The use of image sensors as an automation tool for the IIoT is increasingly becoming more common. Due to the fact that this organisation transfers an enormous number of photographs at any one time, one of the most significant issues that it has is reducing the total quantity of data that is sent and, as a result, the available bandwidth, without compromising the image quality. Image compression in the sensor, on the other hand, expedites the transfer of data while simultaneously reducing bandwidth use. The traditional method of protecting sensitive data is rendered less effective in an environment dominated by IoT owing to the involvement of third parties. The image encryption model provides a safe and adaptable method to protect the confidentiality of picture transformation and storage inside an IIoT system. This helps to ensure that image datasets are kept safe. The Linde-Buzo-Gray (LBG) methodology is an example of a vector quantization algorithm that is extensively used and a relatively new form of picture reduction known as vector quantization (VQ). As a result, the purpose of this research is to create an artificial humming bird optimization approach that combines LBG-enabled codebook creation and encryption (AHBO-LBGCCE) for use in an IIoT setting. In the beginning, the AHBO-LBGCCE method used the LBG model in conjunction with the AHBO algorithm in order to construct the VQ. The Burrows-Wheeler Transform (BWT) model is used in order to accomplish codebook compression. In addition, the Blowfish algorithm is used in order to carry out the encryption procedure so that security may be attained. A comprehensive experimental investigation is carried out in order to verify the effectiveness of the proposed algorithm in comparison to other algorithms. The experimental values ensure that the suggested approach and the outcomes are examined in a variety of different perspectives in order to further enhance them.

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Section: Related Workmentioning

confidence: 99%

Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

Kumar¹,

Srinivasan²

2023

Intelligent Automation &Amp; Soft Computing

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“…Robust watermarking is suitable for ownership protection because the algorithm is robust against possible attacks. Fragile watermarking is suitable for detecting tampering [17]. The secure communication of bank documents and protection against tampering is necessary for banking applications.…”

Section: Background and Related Workmentioning

confidence: 99%

Cyber-Physical Customer Management for Internet of Robotic Things-Enabled Banking

2023

Self Cite

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In-person banking is still an important part of financial services around the world. Hybrid bank branches with service robots can improve efficiency and reduce operating costs. An efficient autonomous Know-Your-Customer (KYC) is required for hybrid banking. In this paper, an automated deep learning-based framework for interbank KYC in robot-based cyber-physical banking is proposed. A deep biometric architecture was used to model the customer's KYC and anonymise the collected visual data to ensure the customer's privacy. The symmetric-asymmetric encryption-decryption module in addition to the blockchain network was used for secure and decentralized transmission and validation of the biometric information. A high-capacity fragile watermarking algorithm based on the integer-to-integer discrete wavelet transform in combination with the Z6 and A6 lattice vector quantization for the secure transmission and storage of inperson banking documents is also proposed. The proposed framework was simulated and validated using a Pepper humanoid robot for the automated biometric-based collection of handwritten bank checks from customers adhering to COVID-19 pandemic safety guidelines. The biometric information of bank customers such as fingerprint and name is embedded as a watermark in the related bank documents using the proposed framework. The results show that the proposed security protection framework can embed more biometric data in bank documents in comparison with similar algorithms. Furthermore, the quality of the secured bank documents is 20% higher in comparison with other proposed algorithms. Also, the hierarchal visual information communication and storage module that anonymizes the identity of people in videos collected by robots can satisfy the privacy requirements of the banks. Overall, the proposed framework can provide a rapid, efficient, and cost-effective inter-bank solution for future in-person banking while adhering to the security requirements and banking regulations.

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“…The new proposed algorithm is an improved version of the watermarking algorithm proposed by the authors [11] based on similar sub-lattices of A 4 LVQ, with higher capacity as well as higher imperceptibility. The proposed algorithm can embed more than 20 KB of data in a 512 × 512 grayscale image, which is more than other watermarking algorithms while keeping the quality of the cover image at an acceptable level of Peak Signal to Noise Ratio (PSNR).…”

Section: Introductionmentioning

confidence: 99%

Secure Medical Image Communication Using Fragile Data Hiding Based on Discrete Wavelet Transform and A₅ Lattice Vector Quantization

2023

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Secure communication of medical images is essential to telemedicine. Message Authentication Codes (MAC) can be embedded inside medical images to protect their integrity. Fragile watermarking algorithms are suitable options since they can be used to detect any tampering attempt. In this paper, a novel fragile data-hiding algorithm based on Integer-to-Integer Discrete Wavelet Transforms (IIDWT) and A 5 Lattice Vector Quantization (LVQ) is proposed. In the proposed data-hiding algorithm, a combination of the medical image Metadata and a MAC is embedded into the medical image. The Metadata includes information about the patient such as name, family, birthday, the place where it is created such as the name of the hospital, and the physician's name. To preserve the privacy of the patients and the physician/hospital, the Metadata is then replaced with fake information. The receiver can extract the Metadata and the MAC. If the extracted MAC is the same as the expected MAC, the integrity of the medical image is guaranteed. Otherwise, a tampering attempt is detected. The proposed algorithm can embed 50% more data than similar algorithms in medical images while keeping the Peak Signal to Noise Ratio (PSNR) in acceptable ranges. Furthermore, the proposed algorithm is applied to a dataset of medical images and high PSNR values above 53.88 dB are experienced.INDEX TERMS Data hiding, privacy, medical image privacy, fragile data hiding, integer discrete wavelet transforms, lattice vector quantization.

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Fragile high capacity data hiding in digital images using integer-to-integer DWT and lattice vector quantization

Cited by 12 publications

References 49 publications

Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

Cyber-Physical Customer Management for Internet of Robotic Things-Enabled Banking

Secure Medical Image Communication Using Fragile Data Hiding Based on Discrete Wavelet Transform and A₅ Lattice Vector Quantization

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