Real-time watermark reconstruction for the identification of source information based on deep neural network

Sinhal, Rishi; Ansari, Irshad Ahmad; Jain, Deepak Kumar

doi:10.1007/s11554-019-00937-z

Cited by 17 publications

(9 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The current use of EMRs hold some additional key disadvantages as identified in [ 30 – 32 ]. Machine learning- and deep learning-based models for image analysis have been proposed and studied in several studies [ 33 , 34 ]. Chen and Shih [ 35 ] have implemented the architecture of the EMR system that can be conflated with streaming media.…”

Section: Related Workmentioning

confidence: 99%

Artificial intelligence-assisted blockchain-based framework for smart and secure EMR management

Chamola

Goyal

Sharma³

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

Healthcare professionals, patients, and other stakeholders have been storing medical prescriptions and other relevant reports electronically. These reports contain the personal information of the patients, which is sensitive data. Therefore, there exists a need to store these records in a decentralized model (using IPFS and Ethereum decentralized application) to provide data and identity protection. Many patients recurrently visit doctors and undergo treatments while receiving different prescriptions and reports. In case of an emergency, the doctors and attendants may need and benefit from the patients’ medical history. However, they are unable to go through medical history and a wide range of previous reports and prescriptions due to time constraints. In this paper, we propose an AI-assisted blockchain-based framework in which the stored medical records (handwritten prescriptions, printed prescriptions, and printed reports) are stored and processed using various AI techniques like optical character recognition (OCR) to form a single patient medical history report. The report concisely presents only the crucial information for convenience and perusal and is stored securely over a decentralized blockchain network for later use.

show abstract

Section: Related Workmentioning

confidence: 99%

Artificial intelligence-assisted blockchain-based framework for smart and secure EMR management

Chamola

Goyal

Sharma³

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…It has better ability to model the discontinuous nature and can be used in the image processing applications for the analysis of abrupt changes, texture feature, and the detection of the edges. It is also used in denoising applications very well because it has ability to smoothen the data without compromising the edges [33,36].…”

Section: Slantlet Transformmentioning

confidence: 99%

Multipurpose medical image watermarking for effective security solutions

Sinhal

Sharma²,

Ansari

et al. 2022

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

Digital medical images contain important information regarding patient's health and very useful for diagnosis. Even a small change in medical images (especially in the region of interest (ROI)) can mislead the doctors/practitioners for deciding further treatment. Therefore, the protection of the images against intentional/unintentional tampering, forgery, filtering, compression and other common signal processing attacks are mandatory. This manuscript presents a multipurpose medical image watermarking scheme to offer copyright/ownership protection, tamper detection/localization (for ROI (region of interest) and different segments of RONI (region of non-interest)), and self-recovery of the ROI with 100% reversibility. Initially, the recovery information of the host image's ROI is compressed using LZW (Lempel-Ziv-Welch) algorithm. Afterwards, the robust watermark is embedded into the host image using a transform domain based embedding mechanism. Further, the 256-bit hash keys are generated using SHA-256 algorithm for the ROI and eight RONI regions (i.e. RONI-1 to RONI-8) of the robust watermarked image. The compressed recovery data and hash keys are combined and then embedded into the segmented RONI region of the robust watermarked image using an LSB replacement based fragile watermarking approach. Experimental results show high imperceptibility, high robustness, perfect tamper detection, significant tamper localization, and perfect recovery of the ROI (100% reversibility). The scheme doesn't need original host or watermark information for the extraction process due to the blind nature. The relative analysis demonstrates the superiority of the proposed scheme over existing schemes.

show abstract

“…Recently the deep learning-based image watermarking became popular to achieve high capacity and robustness of the watermarking systems [27][28][29]. The synergetic neural networks based digital image watermarking has proposed in [27] to ensure the security and robustness of the watermarking system.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this algorithm, the cooperative neural network has been used to detect and extract the watermark. In [28], the host image is divided into equal size subblock, and each subblock is transformed using slantlet transformation. Three copies of watermark information are embedded into the cover image.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient low bit rate image watermarking and tamper detection for image authentication

Kabir

2021

SN Appl. Sci.

View full text Add to dashboard Cite

This study presents the vulnerability of digital documents and its effective way to protect the ownership and detection of unauthorized modification of multimedia data. Watermarking is an effective way to protect vulnerable data in a digital environment. In this paper, a watermarking algorithm has been proposed based on a lossy compression algorithm to ensure authentication and detection of forgery. In this proposed method, the CDF9/7 biorthogonal wavelet is used to transform the watermark image and encoded the wavelet coefficients using Set Partition in Hierarchical Tree algorithm. Then, the encoded bits are encrypted by shuffling and encrypting using symmetric keys. After that the encrypted bits are inserted into the Least Significant Bit position of the cover image. In addition, two tamper detection bits are generated based on texture information and pixel location and inserted in the watermarked image. The proposed algorithm reconstructs the watermark and the tampering region more efficiently and achieved 56.5463 dB PSNR for STARE database. Experimental result shows that the proposed algorithm is effectively prevented different attacks and ensure the integrity of watermark bits within the watermarked image. Also finds the tampered region more efficiently compared with the existing state of art algorithms.

show abstract

Real-time watermark reconstruction for the identification of source information based on deep neural network

Cited by 17 publications

References 53 publications

Artificial intelligence-assisted blockchain-based framework for smart and secure EMR management

Artificial intelligence-assisted blockchain-based framework for smart and secure EMR management

Multipurpose medical image watermarking for effective security solutions

An efficient low bit rate image watermarking and tamper detection for image authentication

Contact Info

Product

Resources

About