Deep Machine Learning to Enhance Ann Performance: Fingerprint Classifier Case Study

Abstract

The human fingerprint is very important for human identification. Therefore, many works have been done to enhance the possibility of detecting a human fingerprint. This research article will explain how to build a high-performance fingerprint classification system. In the study presented in this research paper, the Kmeans clustering method will be used for several reasons, the most important of which is the flexibility of this method by controlling the number of values in the features vector of the fingerprint and more than one alternative can be used to form the features, as it is not sensitive to the process of rotating the fingerprint image. The key idea is to normalize the created fingerprint database and exploit the concept of deep learning to minimize the error between the calculated output and the desired target output. The artificial neural network architecture of different types of ANN will be expanded vertically and horizontally. These architectures will be trained and tested; the obtained training and testing results will be used to make some useful recommendations for using an artificial neural network as a recognition tool in the fingerprint recognition system.

Bits Substitution to Secure LSB Method of Data Steganography

“…It provides a higher degree of security that enables the protection of the image (stego) that contains the message from attacks by people who are not authorized to view confidential information. The accuracy and clarity of the image will not change much when the message is included in an image using LSB method, a substitution sequence will be used to create a PK key, this will provide the LSB method with the required security to protect the embedded secrete message [30][31][32][33][34][35][36][37][38][39][40][41].…”

“…-The pixel in the carrier image is slightly affected by the process of hiding the secret message, as the pixel value changes within the range -1 and +1 (see table 1), which is a very small value that cannot be noticed with the naked eye, and by this the MSE between the carrier and stego images will be small, while the PSNR and CC between them will be high [31][32][33][34][35][36][37][38][39][40].…”

Text Files Protection using Chaotic Logistic Keys

“…A good method of data cryptography must provide a low quality in the encryption phase and a high quality in the decryption phase [43][44][45][46][47][48][49][50]. The quality (see figure 1) between two files can be measured by the quality parameters MSE, PSNR, CC and NSCR, the method of cryptography must satisfy the requirements listed in table 1 [22][23][24][25][26][27][28][29][30]: The selected PK to be used in encryption and decryption phases must be kept in secret between the data sender and the data receiver. This key is to be used to generate the secret key (keys) needed to apply text file encryption and decryption.…”

Using Secret Image Block to Secure LSB Data Steganography

“…Secret message steganography is the process of hiding secret message in a covering digital color image to produce a stego_image as shown in figure 1 [55][56][57][58][59][60][61][62][63][64]. Data steganography is required to protect secret data from being used from any other third party who does not have the right to read or deal with the secret data [45][46][47][48][49][50][51][52][53][54]. The data sender usually uses one of the existing methods of data steganography to hide secret message, and then the receiver uses the same method to extract secret message from the stego image [21][22][23][24].…”

“…The data sender usually uses one of the existing methods of data steganography to hide secret message, and then the receiver uses the same method to extract secret message from the stego image [21][22][23][24]. Mostly Digital color image is the most popular data used as covering media to hide SMs due to following reasons [33][34][35][36][37][38][39]:…”