A Hybrid Concept of Cryptography and Dual Watermarking (LSB_DCT) for Data Security

2018 ◽  
Vol 12 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Ranjeet Kumar Singh ◽  
Dilip Kumar Shaw
Keyword(s):  
Data Security ◽  
Rapid Development ◽  
Hybrid Approach ◽  
Image Data ◽  
Image Authentication ◽  
Multimedia Systems ◽  
Digital Data ◽  
Secret Data ◽  
Data Authentication ◽  
Dual Watermarking

Now a days, in communication technology safety of digital data in the form of text, image, and video, audio is a biggest problem. With the rapid development of the network multimedia systems, security is the biggest issue. Digital watermarking is one of the solutions to these problems. It hides some secret data into the original image and this information is use for image authentication and security. This paper Focus an application using Hybrid approach of Cryptography technique and dual watermarking for the purpose of Providing highly security and authentication of digital data. This paper use cryptography and QR Code in combined approach of LSB and DCT Digital image water marking technique. The Experimental results are given in the form of table and graph. This algorithm provides more security and data authentication compare to other image data security approach.

A Hybrid Concept of Cryptography and Dual Watermarking (LSB_DCT) for Data Security

2021 ◽  
pp. 854-867
Author(s):  
Ranjeet Kumar Singh ◽  
Dilip Kumar Shaw
Keyword(s):  
Data Security ◽  
Rapid Development ◽  
Hybrid Approach ◽  
Image Authentication ◽  
Multimedia Systems ◽  
Digital Data ◽  
Secret Data ◽  
Data Authentication ◽  
Hybrid Concept ◽  
Dual Watermarking

Now a days, in communication technology safety of digital data in the form of text, image, and video, audio is a biggest problem. With the rapid development of the network multimedia systems, security is the biggest issue. Digital watermarking is one of the solutions to these problems. It hides some secret data into the original image and this information is use for image authentication and security. This paper Focus an application using Hybrid approach of Cryptography technique and dual watermarking for the purpose of Providing highly security and authentication of digital data. This paper use cryptography and QR Code in combined approach of LSB and DCT Digital image water marking technique. The Experimental results are given in the form of table and graph. This algorithm provides more security and data authentication compare to other image data security approach.

scholarly journals Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

scholarly journals Identifying Habitat Elements from Bird Images Using Deep Convolutional Neural Networks

Animals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1263
Author(s):  
Zhaojun Wang ◽  
Jiangning Wang ◽  
Congtian Lin ◽  
Yan Han ◽  
Zhaosheng Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Digital Technology ◽  
Rapid Development ◽  
Image Data ◽  
Image Database ◽  
Test Accuracy ◽  
Practical Application ◽  
Accuracy Rate ◽  
Deep Convolutional Neural Networks

With the rapid development of digital technology, bird images have become an important part of ornithology research data. However, due to the rapid growth of bird image data, it has become a major challenge to effectively process such a large amount of data. In recent years, deep convolutional neural networks (DCNNs) have shown great potential and effectiveness in a variety of tasks regarding the automatic processing of bird images. However, no research has been conducted on the recognition of habitat elements in bird images, which is of great help when extracting habitat information from bird images. Here, we demonstrate the recognition of habitat elements using four DCNN models trained end-to-end directly based on images. To carry out this research, an image database called Habitat Elements of Bird Images (HEOBs-10) and composed of 10 categories of habitat elements was built, making future benchmarks and evaluations possible. Experiments showed that good results can be obtained by all the tested models. ResNet-152-based models yielded the best test accuracy rate (95.52%); the AlexNet-based model yielded the lowest test accuracy rate (89.48%). We conclude that DCNNs could be efficient and useful for automatically identifying habitat elements from bird images, and we believe that the practical application of this technology will be helpful for studying the relationships between birds and habitat elements.

scholarly journals Reversible Data Hiding in Encrypted Images Using Median Edge Detector and Two’s Complement

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 921
Author(s):  
Rui Wang ◽  
Guohua Wu ◽  
Qiuhua Wang ◽  
Lifeng Yuan ◽  
Zhen Zhang ◽  
...  
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Personal Information ◽  
Rapid Development ◽  
Prediction Method ◽  
Prediction Errors ◽  
Edge Detector ◽  
Secret Data ◽  
Encrypted Images ◽  
Cloud Servers

With the rapid development of cloud storage, an increasing number of users store their images in the cloud. These images contain many business secrets or personal information, such as engineering design drawings and commercial contracts. Thus, users encrypt images before they are uploaded. However, cloud servers have to hide secret data in encrypted images to enable the retrieval and verification of massive encrypted images. To ensure that both the secret data and the original images can be extracted and recovered losslessly, researchers have proposed a method that is known as reversible data hiding in encrypted images (RDHEI). In this paper, a new RDHEI method using median edge detector (MED) and two’s complement is proposed. The MED prediction method is used to generate the predicted values of the original pixels and calculate the prediction errors. The adaptive-length two’s complement is used to encode the most prediction errors. To reserve room, the two’s complement is labeled in the pixels. To record the unlabeled pixels, a label map is generated and embedded into the image. After the image has been encrypted, it can be embedded with the data. The experimental results indicate that the proposed method can reach an average embedding rate of 2.58 bpp, 3.04 bpp, and 2.94 bpp on the three datasets, i.e., UCID, BOSSbase, BOWS-2, which outperforms the previous work.

scholarly journals A secure image steganography based on burrows wheeler transform and dynamic bit embedding

2019 ◽  
Vol 9 (1) ◽  
pp. 460
Author(s):  
Ahmed Toman Thahab
Keyword(s):  
Communication Networks ◽  
Color Image ◽  
Image Steganography ◽  
Digital Data ◽  
Embedding Capacity ◽  
Image Size ◽  
Secret Data ◽  
Exclusive Or ◽  
Burrows Wheeler Transform ◽  
Color Image Steganography

In modern public communication networks, digital data is massively transmitted through the internet with a high risk of data piracy. Steganography is a technique used to transmit data without arousing suspicion of secret data existence.  In this paper, a color image steganography technique is proposed in spatial domain. The cover image is segmented into non-overlapping blocks which are scattered among image size window using Burrows Wheeler transform before embedding. Secret data is embedded in each block according to its sequence in the Burrows Wheeler transform output. The hiding method is an operation of an exclusive-or between a virtual bit which is generated from the most significant bit and the least significant bits of the cover pixel. Results of the algorithm are analyzed according to its degradation of the output image and embedding capacity. The results are also compared with other existing methods.

scholarly journals Is Big Data Security Essential for Students to Understand?

2020 ◽  
Vol 11 (2) ◽  
pp. 161-170
Author(s):  
Rochman Hadi Mustofa
Keyword(s):  
Big Data ◽  
Undergraduate Students ◽  
Data Security ◽  
Personal Data ◽  
Digital Data ◽  
Security And Privacy ◽  
Group Discussions ◽  
Technology Applications ◽  
Digital Payments ◽  
Financial Technology

AbstractBig Data has become a significant concern of the world, along with the era of digital transformation. However, there are still many young people, especially in developing countries, who are not yet aware of the security of their big data, especially personal data. Misuse of information from big data often results in violations of privacy, security, and cybercrime. This study aims to determine how aware of the younger generation of security and privacy of their big data. Data were collected qualitatively by interviews and focus group discussions (FGD) from. Respondents were undergraduate students who used social media and financial technology applications such as online shopping, digital payments, digital wallet and hotel/transportation booking applications. The results showed that students were not aware enough and understood the security or privacy of their digital data, and some respondents even gave personal data to potentially scam sites. Most students are not careful in providing big data information because they are not aware of the risks behind it, socialization is needed in the future as a step to prevent potential data theft.

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