BlessMark: a blind diagnostically-lossless watermarking framework for medical applications based on deep neural networks

Zarrabi, Hamidreza; Emami, Ali; Khadivi, Pejman; Karimi, Nader; Samavi, Shadrokh

doi:10.1007/s11042-020-08698-9

Cited by 6 publications

(6 citation statements)

References 32 publications

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“…Zarrabi et al present a blind diagnostically-lossless watermarking (BlessMark) [2]. In this research the region of interest (ROI) map is generated by a deep network and the watermark is only embedded in the region of non-interest (RONI) and the ROI is kept intact.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

“…Thus, the integrity and authenticity of data and software often need to be protected. Over the past decades, medical image privacy protection based on data hiding or watermarking has been a common method [1], [2]. Specifically, authentication information can be hidden in the marked images and retrieved when needed.…”

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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“…The secure communication of bank documents and protection against tampering is necessary for banking applications. Zarrabi et al [18] proposed blind and diagnostically lossless watermarking (BlessMark), and Fares et al [19] proposed a blind and robust watermarking algorithm based on the DCT and Schur decomposition. Agarwal and Singh [20] presented a watermarking method by combining the DCT and genetic algorithms for optimization.…”

Section: Background and Related Workmentioning

confidence: 99%

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

2023

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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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“…Furthermore, many 2D image watermarking techniques use a template [22] or feature points [23] for robustness against geometric distortion. Recently, 2D image watermarking techniques have been introduced that use deep learning [24]- [26].…”

Section: A 2d Image Watermarkingmentioning

confidence: 99%

Robust Spherical Panorama Image Watermarking Against Viewpoint Desynchronization

Kang

Hou

et al. 2020

IEEE Access

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Although quantitative and qualitative growth of spherical panorama content has been achieved, watermarking methods for copyright protection are still insufficient. The spherical panorama image watermarking technique must be able to detect a watermark from the viewed images, but it is difficult to detect the watermark directly from the images due to various distortions. We propose a method for recovering the watermark-detection target viewed image to an equirectangular-formed source image and detecting the watermark after recovery. For recovery, we propose combining the scale-invariant feature transform point matching and the shift and rotation estimation method using a Euclidean transformation matrix. For watermark embedding and detection, a discrete Fourier transform magnitude coefficient is used. With the shift-invariant characteristic, our proposed method is robust against viewpoint desynchronization. Because finding an accurate viewpoint is difficult, our method offers a substantial advantage in the spherical panorama image watermarking scheme. Furthermore, it allows watermark detection on a continuous viewpoint spherical panorama system. In addition, our experiments reveal that our method is robust against signal attacks, such as JPEG compression, Gaussian filter blurring, noise addition, and histogram equalization.

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BlessMark: a blind diagnostically-lossless watermarking framework for medical applications based on deep neural networks

Cited by 6 publications

References 32 publications

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

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

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

Robust Spherical Panorama Image Watermarking Against Viewpoint Desynchronization

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