scholarly journals Block Based Symmetry Key Visual Cryptography

2012 ◽  
Vol 4 (9) ◽  
pp. 10-19 ◽  
Author(s):  
Satyendra Nath Mandal ◽  
Subhankar Dutta ◽  
Ritam Sarkar
Keyword(s):  
Visual Cryptography ◽  
Block Based

Robust progressive block based visual cryptography with chaotic map

2016 ◽  
Vol 19 (5-6) ◽  
pp. 1025-1040 ◽  
Author(s):  
Dhiraj Pandey ◽  
Uma Shankar Rawat ◽  
Anil Kumar
Keyword(s):  
Chaotic Map ◽  
Visual Cryptography ◽  
Block Based

scholarly journals Constructions and Properties of General (<i>k, n</i>) Block-Based Progressive Visual Cryptography

ETRI Journal ◽  
2015 ◽  
Vol 37 (5) ◽  
pp. 979-989 ◽  
Author(s):  
Ching-Nung Yang ◽  
Chih-Cheng Wu ◽  
Yi-Chin Lin ◽  
Cheonshik Kim
Keyword(s):  
Visual Cryptography ◽  
Block Based

scholarly journals A Visual Cryptography-Based Watermarking Approach for the Detection and Localization of Image Forgery

Electronics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 136
Author(s):  
Moataz Z. Salim ◽  
Ali J. Abboud ◽  
Remzi Yildirim
Keyword(s):  
Digital Images ◽  
Medical Image Analysis ◽  
Visual Cryptography ◽  
Experimental Results ◽  
Walsh Transform ◽  
Discrete Wavelet ◽  
Image Block ◽  
Geometric Attacks ◽  
Block Based ◽  
Detection And Localization

The usage of images in different fields has increased dramatically, especially in medical image analysis and social media. Many risks can threaten the integrity and confidentiality of digital images transmitted through the internet. As such, the preservation of the contents of these images is of the utmost importance for sensitive healthcare systems. In this paper, the researchers propose a block-based approach to protect the integrity of digital images by detecting and localizing forgeries. It employs a visual cryptography-based watermarking approach to provide the capabilities of forgery detection and localization. In this watermarking scheme, features and key and secret shares are generated. The feature share is constructed by extracting features from equal-sized blocks of the image by using a Walsh transform, a local binary pattern and a discrete wavelet transform. Then, the key share is generated randomly from each image block, and the secret share is constructed by applying the XOR operation between the watermark, feature share and key share. The CASIA V 1.0 and SIPI datasets were used to check the performance and robustness of the proposed method. The experimental results from these datasets revealed that the percentages of the precision, recall and F1 score classification indicators were approximately 97% for these indicators, while the percentages of the TAF and NC image quality indicators were approximately 97% and 96% after applying several known image processing and geometric attacks. Furthermore, the comparative experimental results with the state-of-art approaches proved the robustness and noticeable improvement in the proposed approach for the detection and localization of image forgeries in terms of classification and quality measures.

A Structure Learning Method for Block-Based Neural Networks

2013 ◽  
Vol 133 (10) ◽  
pp. 1976-1982 ◽  
Author(s):  
Hidetaka Watanabe ◽  
Seiichi Koakutsu ◽  
Takashi Okamoto ◽  
Hironori Hirata
Keyword(s):  
Neural Networks ◽  
Structure Learning ◽  
Learning Method ◽  
Block Based

Block-Based Incremental Caching for Information Centric Networking

2016 ◽  
Vol E99.B (12) ◽  
pp. 2550-2558
Author(s):  
Sung-Hwa LIM ◽  
Yeo-Hoon YOON ◽  
Young-Bae KO ◽  
Huhnkuk LIM
Keyword(s):  
Information Centric Networking ◽  
Block Based
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