A New Generative Mathematical Model for Coverless Steganography System Based on Image Generation

The ability of any steganography system to correctly retrieve the secret message is the primary criterion for measuring its efficiency.Recently,researchers have tried to generate a new natural image driven from only the secret message bits rather than using a cover to embed the secret message within it;this is called the stego image.This paper proposes a new secured coverless steganography system using a generative mathematical model based on semi Quick Response(QR)code and maze game image generation.This system consists of two components.The first component contains two processes,encryption process,and hiding process.The encryption process encrypts secret message bits in the form of a semi-QR code image whereas the hiding process conceals the pregenerated semi-QR code in the generated maze game image.On the other hand,the second component contains two processes,extraction and decryption,which are responsible for extracting the semi-QR code from the maze game image and then retrieving the original secret message from the extracted semi-QR code image,respectively.The results were obtained using the bit error rate(BER)metric.These results confirmed that the system achieved high hiding capacity,good performance,and a high level of robustness against attackers compared with other coverless steganography methods.

An Automatic Deep Neural Network Model for Fingerprint Classification

The accuracy offingerprint recognition model is extremely important due to its usage in forensic and securityfields.Anyfingerprint recognition system has particular network architecture whereas many other networks achieve higher accuracy.To solve this problem in a unified model,this paper proposes a model that can automatically specify itself.So,it is called an automatic deep neural net-work(ADNN).Our algorithm can specify the appropriate architecture of the neur-al network used and some significant parameters of this network.These parameters are the number offilters,epochs,and iterations.It guarantees the high-est accuracy by updating itself until achieving 99%accuracy then it stops and out-puts the result.Moreover,this paper proposes an end-to-end methodology for recognizing a person’s identity from the inputfingerprint image based on a resi-dual convolutional neural network.It is a complete system and is fully automated whether in the features extraction stage or the classification stage.Our goal is to automate thisfingerprint recognition system because the more automatic the sys-tem is,the more time and effort it saves.Our model also allows users to react by inputting the initial values of these parameters.Then,the model updates itself until itfinds the optimal values for the parameters and achieves the best accuracy.Another advantage of our algorithm is that it can recognize people from their thumb and otherfingers and its ability to recognize distorted samples.Our algo-rithm achieved 99.75%accuracy on the publicfingerprint dataset(SOCOFing).This is the best accuracy compared with other models.

Handling Big Data in Relational Database Management Systems

Currently, relational database management systems (RDBMSs)face different challenges in application development due to the massive growthof unstructured and semi-structured data. This introduced new DBMS categories, known as not only structured query language (NoSQL) DBMSs, whichdo not adhere to the relational model. The migration from relational databasesto NoSQL databases is challenging due to the data complexity. This study aimsto enhance the storage performance of RDBMSs in handling a variety of data.The paper presents two approaches. The first approach proposes a convenientrepresentation of unstructured data storage. Several extensive experimentswere implemented to assess the efficiency of this approach that could resultin substantial improvements in the RDBMSs storage. The second approachproposes using the JavaScript Object Notation (JSON) format to representmultivalued attributes and many to many (M:N) relationships in relationaldatabases to create a flexible schema and store semi-structured data. Theresults indicate that the proposed approaches outperform similar approachesand improve data storage performance, which helps preserve software stabilityin huge organizations by improving existing software packages whose replacement may be highly costly.