Copy-move forgery detection using convolutional neural network and K-mean clustering

Copying and pasting a patch of an image to hide or exaggerate something in a digital image is known as a copy-move forgery. Copy-move forgery detection (CMFD) is hard to detect because the copied part image from a scene has similar properties with the other parts of the image in terms of texture, light illumination, and objective. The CMFD is still a challenging issue in some attacks such as rotation, scaling, blurring, and noise. In this paper, an approach using the convolutional neural network (CNN) and k-mean clustering is for CMFD. To identify cloned parts candidates, a patch of an image is extracted using corner detection. Next, similar patches are detected using a pre-trained network inspired by the Siamese network. If two similar patches are not evidence of the CMFD, the post-process is performed using k-means clustering. Experimental analyses are done on MICC-F2000, MICC-F600, and MICC-F8 databases. The results showed that using the proposed algorithm we can receive a 94.13% and 96.98% precision and F1 score, respectively, which are the highest among all state-of-the-art algorithms

A New MATLAB GUI Tool for Instructing Operation of Power Electronic Rectifiers

This study seeks to present an interactive tool, which exploits the GUI related abilities in MATLAB, to investigate power electronic rectifiers operation. The present paper aims to show a flexible and extendable environment for steady state simulation of ideal controlled, uncontrolled, single-phase and three-phase power electronic rectifiers. This is accomplished at the presence of R, L and E loads with or without a flywheel diode. The easy application of our tool makes it feasible to be used by the teacher in the classroom. Also its short running time and the PSpice output netlist make it a remarkable alternative to the MATLAB PowerSim toolbox and PSIM software for studying ac-dc converters. Furthermore, in this paper some basic equations were introduced to analyze single and three-phase rectifiers. Because these equations are common between ac-dc converters, analyzing the different types of rectifiers will be easy for the students. The penultimate issue refers that, the proposed program can cause a variety of single and three phase controlled rectifiers accompanied by various combinations of R, L and E loads to be simulated. Ultimately, obtained results are compared to another well-known simulator’s such as PSpice to verify their accuracy

Design and Analysis of the Voltage Controller for the Non Isolated Boost DC-DC Convertor

In this paper, a controller has been presented by the root locus method based on the state space average model of the boost switching regulator with all of the converter’s parameters and uncertainties. In this model, the load current is unknown and the inductor, capacitor, diode and active switch are non ideal and have an on-state resistance. Furthermore, an on-state voltage drop has been considered for diode and active switch. By neglecting the load current and assuming the ideal elements the simplified model of the regulator has been caddied out. By these complete and simplified models, a step by step method has been proposed to design a single input single output (SISO), second order controller based on roots locus method. In this regard the controller's electronic circuit has been introduced by operational amplifiers. At the end, by simulation of the complete closed-loop system in MATLAB Simulink environment and comparing its results by the results of the regulator and controller circuits in PLECS, the accuracy of the designed controller performance has been shown

Facial recognition using new LBP representations

In this paper, we propose a facial recognition based on the LBP operator. We divide the face into non-overlapped regions. After that, we classify a training set using each region at a time under different configurations of the LBP operator. Regarding to the best recognition rate, we consider a weight and specific LBP configuration to the regions. To represent the face image, we extract LBP histograms with the specific configuration (radius and neighbors) and concatenate them into feature histogram. We propose a multi-resolution approach, to gather local and global information and improve the recognition rate. To evaluate our proposed approach, we considered the FERET data set, which includes different facial expressions, lighting, and aging of the subjects. In addition, weighted Chi-2 is considered as a dissimilarity measure. The experimental results show a considerable improvement against the original idea

Efficient levels of spatial pyramid representation for local binary patterns

Local binary patterns (LBPs) are a well‐known operator that shows the ability for rotation and scale invariant texture classification. A recent extension of this operator is the pyramid transform domain approach on LBPs (PLBP). Obtaining more accuracy by using more pyramid representations is an important result of PLBP, which increases not only feature dimensionality, but also classification computational time (CT). This study illustrates that more pyramid image representations will not improve the performance of the PLBP. We evaluate efficient levels of representation for the PLBP descriptor. In addition, the authors propose some feature selection approaches, such as the multi‐level and multi‐resolution (ML + MR) approach and the ML, MR and multi‐band (ML + MR + MB) approach and discuss their efficiency and CT. Experimental results show that the proposed feature selection approaches improve the accuracy of texture classification with fewer pyramid image representations. In addition, replacing the Chi‐2 similarity measurement with Czekannowski improves the accuracy of texture classification