scholarly journals Code Division Multiplexing and Machine Learning Based Reversible Data Hiding Scheme for Medical Image

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Bin Ma ◽  
Bing Li ◽  
Xiao-Yu Wang ◽  
Chun-Peng Wang ◽  
Jian Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Hiding ◽  
Reversible Data Hiding ◽  
Medical Image ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Data Embedding ◽  
Secret Data ◽  
Spreading Sequences ◽  
Code Division Multiplexing

In this paper, a new reversible data hiding (RDH) scheme based on Code Division Multiplexing (CDM) and machine learning algorithms for medical image is proposed. The original medical image is firstly converted into frequency domain with integer-to-integer wavelet transform (IWT) algorithm, and then the secret data are embedded into the medium frequency subbands of medical image robustly with CDM and machine learning algorithms. According to the orthogonality of different spreading sequences employed in CDM algorithm, the secret data are embedded repeatedly, most of the elements of spreading sequences are mutually canceled, and the proposed method obtained high data embedding capacity at low image distortion. Simultaneously, the to-be-embedded secret data are represented by different spreading sequences, and only the receiver who has the spreading sequences the same as the sender can extract the secret data and original image completely, by which the security of the RDH is improved effectively. Experimental results show the feasibility of the proposed scheme for data embedding in medical image comparing with other state-of-the-art methods.

scholarly journals Generalized Reversible Data Hiding with Content-Adaptive Operation and Fast Histogram Shifting Optimization

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 917
Author(s):  
Limengnan Zhou ◽  
Hongyu Han ◽  
Hanzhou Wu
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Hot Spot ◽  
Data Embedding ◽  
Histogram Shifting ◽  
Secret Data ◽  
Content Selection ◽  
Wide Range ◽  
Map Generation ◽  
Content Adaptive

Reversible data hiding (RDH) has become a hot spot in recent years as it allows both the secret data and the raw host to be perfectly reconstructed, which is quite desirable in sensitive applications requiring no degradation of the host. A lot of RDH algorithms have been designed by a sophisticated empirical way. It is not easy to extend them to a general case, which, to a certain extent, may have limited their wide-range applicability. Therefore, it motivates us to revisit the conventional RDH algorithms and present a general framework of RDH in this paper. The proposed framework divides the system design of RDH at the data hider side into four important parts, i.e., binary-map generation, content prediction, content selection, and data embedding, so that the data hider can easily design and implement, as well as improve, an RDH system. For each part, we introduce content-adaptive techniques that can benefit the subsequent data-embedding procedure. We also analyze the relationships between these four parts and present different perspectives. In addition, we introduce a fast histogram shifting optimization (FastHiSO) algorithm for data embedding to keep the payload-distortion performance sufficient while reducing the computational complexity. Two RDH algorithms are presented to show the efficiency and applicability of the proposed framework. It is expected that the proposed framework can benefit the design of an RDH system, and the introduced techniques can be incorporated into the design of advanced RDH algorithms.

FPGA Realization of a Reversible Data Hiding Scheme for 5G MIMO-OFDM System by Chaotic Key Generation-Based Paillier Cryptography Along with LDPC and Its Side Channel Estimation Using Machine Learning Technique

2021 ◽  
Author(s):  
Francis H. Shajin ◽  
P. Rajesh
Keyword(s):  
Machine Learning ◽  
Data Hiding ◽  
Reversible Data Hiding ◽  
Performance Metrics ◽  
Security Analysis ◽  
Key Generation ◽  
Data Embedding ◽  
Machine Learning Technique ◽  
Packet Delivery ◽  
Learning Technique

Multiple-Input and Multiple-Output (MIMO) technology is a significant and timely subject, which is highly motivated by the needs of 5G wireless communications. Data transmission performs MIMO, which is highly sensitive. There are several security issues while transmitting the data such as loss of data and code injection. Two efficient methods are Encryption and Data Hiding protection of data in wireless communication. This dissertation suggests FPGA Implementation of RDHS by Chaotic Key Generation-Based Paillier Cryptography with LDPC using machine learning technique. RDHS stands for Reversible Data Hiding Scheme. In a reversible method, the initial stage of preprocessing is to shrink the histogram of image before the process of encryption. Hence, the plaintext domain changing the encrypted images to data embedding cannot result from any pixel repletion. A little distortion data embedding may be taken as the original image may recover the directly decrypted image. Here, the performance metrics of throughput, area consumed, latency, delay, packet delivery, network life and overhead are calculated. The proposed Paillier homomorphic cryptosystem proposes higher network throughput as 99%, higher network life 98%, lower delay rate as 60%, packet delivery as 74%, overhead as 66%, latency as 55% and area consumed as 61% with the existing method such as McEliece, Elgamal and Elliptic curve cryptosystem in the security analysis of the proposed method providing decryption time 94% and encryption time 98% better than the existing method.

scholarly journals A New Kind of High Capacity and Security Reversible Data Hiding Scheme

2019 ◽  
Vol 11 (4) ◽  
pp. 118-129
Author(s):  
Bin Ma ◽  
Xiao-Yu Wang ◽  
Bing Li
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
High Capacity ◽  
Gain Factor ◽  
Embedding Capacity ◽  
Secret Data ◽  
Spreading Sequence ◽  
High Embedding Capacity ◽  
Code Division Multiple ◽  
Spreading Sequences

A novel high capacity and security reversible data hiding scheme is proposed in this article, in which the secret data is represented by different orthogonal spreading sequences and repeatedly embedded into the cover image without disturbing each other in the light of Code Division Multiple Access (CDMA) technique, and thus the embedding capacity is enlarged. As most elements of orthogonal spreading sequences are mutually canceled in the process of repeated embedding, it keeps the distortion of the embedded image at a low level even with high embedding capacity. Moreover, only the receiver who has the spreading sequence and the embedding gain factor the same as the sender can extract the secret data and achieve the original image exactly, thus the proposed scheme achieves high embedding security than other schemes. The results of the experiment demonstrates that the CDMA based reversible data hiding scheme could achieve higher image quality at moderate-to-high embedding capacity compared with other state-of-the-art schemes.

A Reversible Data Hiding for JPEG Images

2012 ◽  
Vol 433-440 ◽  
pp. 4615-4620 ◽  
Author(s):  
Gui Long Liao ◽  
Xin Peng Zhang
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Rate Distortion ◽  
Previous Method ◽  
Data Embedding ◽  
Original Image ◽  
Secret Data ◽  
Jpeg Images ◽  
Efficient Data ◽  
Dct Coefficients

A novel reversible data hiding technique for JPEG images is proposed in this paper. Consecutive zeros in the tail of DCT coefficient sequence in each block are exploited to embed a number of secret bits by modifying only one coefficient. Thanks to the efficient data embedding in the DCT coefficients, the proposed scheme can provide a good rate-distortion performance. Also, when having an image containing secret data, one can perfectly recover the original image after extracting the embedded data. Experimental results show that the proposed scheme outperforms the previous method.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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