Effective Self-Recovery and Tampering Localization Fragile Watermarking for Medical Images

Su, Guo-Dong; Chang, Chin‐Chen; Lin, Chia-Chen

doi:10.1109/access.2020.3019832

Cited by 25 publications

(20 citation statements)

References 40 publications

(112 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, NCC (normal cross correlation) [33], as another method for measuring the similarity of two data sequences, is also measured in our experiments. It ranges from 0 to 1 and is defined by:

NCC false(I, S I false) = \frac{\sum_{i}^{H} {false\sum}_{j}^{W} false(I_{i, j} - μ_{I} false) \cdot false(S I_{i, j} - μ_{S I} false)}{\sqrt{{false\sum}_{i}^{H} \sum_{j}^{W} {(I_{i, j} - μ_{I})}^{2}} \cdot \sqrt{{false\sum}_{i}^{H} \sum_{j}^{W} {(S I_{i, j} - μ_{S I})}^{2}}} .

If NCC = 1, it indicates two images are the same.…”

Section: Resultsmentioning

confidence: 99%

Secure high capacity tetris‐based scheme for data hiding

Chang

Lin³

et al. 2020

IET image process

View full text Add to dashboard Cite

Information hiding is a technique that conceals private information in a trustable carrier, making it imperceptible to unauthorised people. This technique has been used extensively for secure transmissions of multimedia, such as videos, animations, and images. This study proposes a novel Tetris‐based data hiding scheme to flexibly hide more secret messages while ensuring message security. First, an LQ × LQ square lattice Q is selected to determine the maximum embedding capacity, and then it is filled without gaps through rotating and sliding tetrominoes while making the shape of each tetromino different. Secondly, according to the decided Q, the reference matrix and corresponding look‐up table are constructed and then used for secret messages embedding and extraction. In the authors approach, each pixel pair of the original image can be processed to conceal 4‐ or 6‐bit secret messages. The experimental results show that their proposed Tetris‐based scheme has excellent performance, exceeding the performance of some state‐of‐the‐art schemes in both embedding capacity and visual quality. The proposed scheme also provides secure covert communication.

show abstract

“…Finally, NCC (normal cross correlation) [33], as another method for measuring the similarity of two data sequences, is also measured in our experiments. It ranges from 0 to 1 and is defined by:

NCC false(I, S I false) = \frac{\sum_{i}^{H} {false\sum}_{j}^{W} false(I_{i, j} - μ_{I} false) \cdot false(S I_{i, j} - μ_{S I} false)}{\sqrt{{false\sum}_{i}^{H} \sum_{j}^{W} {(I_{i, j} - μ_{I})}^{2}} \cdot \sqrt{{false\sum}_{i}^{H} \sum_{j}^{W} {(S I_{i, j} - μ_{S I})}^{2}}} .

If NCC = 1, it indicates two images are the same.…”

Section: Resultsmentioning

confidence: 99%

Secure high capacity tetris‐based scheme for data hiding

Chang

Lin³

et al. 2020

IET image process

View full text Add to dashboard Cite

show abstract

“…For this, in our approach, each morphed image can carry around log2(2 8 (2 8 +1)/2) ≈ 15 bits secret message, which is greater than the results provided by schemes in [28; 30]. Of course, when N is 2 15 , our approach can carry around log2(2 15 (2 15 +1)/2) ≈ 29 bits secret message, which are 14 bits higher than that of the scheme [3]. Also, we can observe that the hiding capacity of the proposed scheme is lower than that of schemes in [33; 36].…”

Section: Loss Functionmentioning

confidence: 87%

“…The spatial domainbased method is probably the most intuitive of data hiding options that straightly embed secret messages into pixel values of the original cover image. Some popular kinds of data hiding algorithms in the spatial domain are the least significant bit (LSB) [4; 9], prediction error [5], histogram-based approaches [7], modulo operation [10][11][12], and quantization-based methods [15]. Generally speaking, the spatial domain has a higher embedding capacity.…”

Section: Introductionmentioning

confidence: 99%

Coverless Image Steganography Using Morphed Face Recognition based on Convolutional Neural Network

Li,

Chang,

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

In recent years, information security has become a prime issue of worldwide concern. To improve the validity and proficiency of the image data hiding approach, a piece of state-of-the-art secret information hiding transmission scheme based on morphed face recognition is proposed. In our proposed data hiding approach, a group of morphed face images is produced from an arranged small-scale face image dataset. Then, a morphed face image which is encoded with a secret message is sent to the receiver. The receiver uses powerful and robust deep learning models to recover the secret message by recognizing the parents of the morphed face images. Furthermore, we design two novel Convolutional Neural Network (CNN) architectures (e.g. MFR-Net V1 and MFR-Net V2) to perform morphed face recognition and achieved the highest accuracy compared with existing networks. Additionally, the experimental results show that the proposed schema has higher retrieval capacity and accuracy and it provides better robustness.

show abstract

“…The dependability in CRF can be achieved with the private key [50] is given by the sender. The CRF will be implanted, such as fragile watermarks [51], within the packets. Fragile watermarks have the capability to detect any modification or tampering of data.…”

Section: Discussionmentioning

confidence: 99%

Morton Filter-Based Security Mechanism for Healthcare System in Cloud Computing

Bhatia

Malhotra²

2021

Healthcare

View full text Add to dashboard Cite

Electronic health records contain the patient’s sensitive information. If these data are acquired by a malicious user, it will not only cause the pilferage of the patient’s personal data but also affect the diagnosis and treatment. One of the most challenging tasks in cloud-based healthcare systems is to provide security and privacy to electronic health records. Various probabilistic data structures and watermarking techniques were used in the cloud-based healthcare systems to secure patient’s data. Most of the existing studies focus on cuckoo and bloom filters, without considering their throughputs. In this research, a novel cloud security mechanism is introduced, which supersedes the shortcomings of existing approaches. The proposed solution enhances security with methods such as fragile watermark, least significant bit replacement watermarking, class reliability factor, and Morton filters included in the formation of the security mechanism. A Morton filter is an approximate set membership data structure (ASMDS) that proves many improvements to other data structures, such as cuckoo, bloom, semi-sorting cuckoo, and rank and select quotient filters. The Morton filter improves security; it supports insertions, deletions, and lookups operations and improves their respective throughputs by 0.9× to 15.5×, 1.3× to 1.6×, and 1.3× to 2.5×, when compared to cuckoo filters. We used Hadoop version 0.20.3, and the platform was Red Hat Enterprise Linux 6; we executed five experiments, and the average of the results has been taken. The results of the simulation work show that our proposed security mechanism provides an effective solution for secure data storage in cloud-based healthcare systems, with a load factor of 0.9. Furthermore, to aid cloud security in healthcare systems, we presented the motivation, objectives, related works, major research gaps, and materials and methods; we, thus, presented and implemented a cloud security mechanism, in the form of an algorithm and a set of results and conclusions.

show abstract

Effective Self-Recovery and Tampering Localization Fragile Watermarking for Medical Images

Cited by 25 publications

References 40 publications

Secure high capacity tetris‐based scheme for data hiding

Secure high capacity tetris‐based scheme for data hiding

Coverless Image Steganography Using Morphed Face Recognition based on Convolutional Neural Network

Morton Filter-Based Security Mechanism for Healthcare System in Cloud Computing

Contact Info

Product

Resources

About