Recently, Photo editing softwares such as digital cameras, Paintshop Pro, and Photoshop digital can create counterfeit images easily. Various techniques for detection of tamper images or forgery images have been proposed in the literature. A form of digital forgery is copy-move image forgery. Copy-move is one of the forgeries and is used wherever you need to cover a part of the image to add or remove information. Copy-move image forgery refers to copying a specific area of an image itself and pasting it into another area of the same image. The purpose of copy-move image forgery detection is to detect the same or very similar region image within the original image. In this paper, we proposed fast detection of forgery image using four step search based on discrete cosine transform and a four step search algorithm using discrete cosine transform (FSSDCT). The computational complexity of our algorithm reduced 34.23 % than conventional DCT three step search algorithm (DCTTSS).

Recently, there have been a lot of researches on the whole face replacement system, but it is not easy to obtain stable results due to various attitudes, angles and facial diversity. To produce a natural synthesis result when replacing the face shown in the video image, technologies such as face area detection, feature extraction, face alignment, face area segmentation, 3D attitude adjustment and facial transposition should all operate at a precise level. And each technology must be able to be interdependently combined. The results of our analysis show that the difficulty of implementing the technology and contribution to the system in facial replacement technology has increased in facial feature point extraction and facial alignment technology. On the other hand, the difficulty of the facial transposition technique and the three-dimensional posture adjustment technique were low, but showed the need for development. In this paper, we propose four facial replacement models such as 2-D Faceswap, OpenPose, Deekfake, and Cycle GAN, which are suitable for the Cox platform. These models have the following features; i.e. these models include a suitable model for front face pose image conversion, face pose image with active body movement, and face movement with right and left side by 15 degrees, Generative Adversarial Network.

To extract the depth map from a single image, a number of CNN-based deep learning methods have been performed in recent research. In this study, the GAN structure of Pix2Pix is maintained. this model allows to converge well, because it has the structure of the generator and the discriminator. But the convolution in this model takes a long time to compute. So we change the convolution form in the generator to a depthwise convolution to improve the speed while preserving the result. Thus, the seven down-sizing convolutional hidden layers in the generator U-Net are changed to depthwise convolution. This type of convolution decreases the number of parameters, and also speeds up computation time. The proposed model shows similar depth map prediction results as in the case of the existing structure, and the computation time in case of a inference is decreased by 64%.

Anthropomorphism is the attribution of human traits, emotions, or intentions to non-human entities. Anthropomorphic animal face masking is the process by which human characteristics are plotted on the animal kind. In this research, we are proposing a compact system which finds the resemblance between a human face and animal face using Deep Convolutional Neural Network (DCNN) and later applies morphism between them. The whole process is done by firstly finding which animal most resembles the particular human face through a DCNN based animal face classification. And secondly, doing triangulation based morphing between the particular human face and the most resembled animal face. Compared to the conventional manual Control Point Selection system using an animator, we are proposing a Viola-Jones algorithm based Control Point selection process which detects facial features for the human face and takes the Control Points automatically. To initiate our approach, we built our own dataset containing ten thousand animal faces and a fourteen layer DCNN. The simulation results firstly demonstrate that the accuracy of our proposed DCNN architecture outperforms the related methods for the animal face classification. Secondly, the proposed morphing method manages to complete the morphing process with less deformation and without any human assistance.

Alberta Stroke Program Early Computed Tomographic Scoring (ASPECTS) is a 10-point CT-scan score designed to quantify early ischemic changes in patients with acute ischemic stroke. However, an assessment of ASPECTS remains a challenge for neuroradiologists in stroke centers. The purpose of this study is to develop an automated ASPECTS scoring system that provides decision-making support by utilizing binary classification with three-dimensional convolutional neural network to analyze CT images. The proposed method consists of three main steps: slice filtering, contrast enhancement and image classification. The experiments show that the obtained results are very promising.

In this paper, a plane detection method using depth information is proposed. 3-D characteristics of a pixel are defined as a direction and length of a normal vector whose is calculated from a plane consisting of a local region centered on the pixel. Image coordinates of each pixel are transformed to 3-D coordinates in order to obtain the local planes. Regions of each plane are detected by calculating similarity of the 3-D characteristics. The similarity of the characteristics consists of direction and distance similarities of normal vectors. If the similarity of the characteristics between two adjacent pixels is enough high, the two pixels are regarded as consisting of same plane. Simulation results show that the proposed method using the depth picture is more accurate for detecting plane areas than the conventional method.

Each year Malaria affects over 200 million people worldwide. Particularly, African continent is highly hit by this disease. According to many researches, this continent is ideal for Anopheles mosquitoes which transmit Malaria parasites to thrive. Rainfall volume is one of the major factor favoring the development of these Anopheles in the tropical Sub-Sahara Africa (SSA). However, the surveillance, monitoring and reporting of this epidemic is still poor and bureaucratic only. In our paper, we proposed a method to fast monitor and report Malaria instances by using Social Network Systems (SNS) and precipitation volume in Nigeria. We used Twitter search Application Programming Interface (API) to live-stream Twitter messages mentioning Malaria, preprocessed those Tweets and classified them into Malaria cases in Nigeria by using Support Vector Machine (SVM) classification algorithm and compared those Malaria cases with average precipitation volume. The comparison yielded a correlation of 0.75 between Malaria cases recorded by using Twitter and average precipitations in Nigeria. To ensure the certainty of our classification algorithm, we used an oversampling technique and eliminated the imbalance in our training Tweets.

As the $4^{th}$ industrial revolution is accelerating, IT convergence application technologies are attracting attention in various fields. In the manufacturing industry, Smart Factory technology, which is blended with IT technology, has been developed to solve the problem casued by the decrease of the labor force, and a monitoring server is required to remotely control the equipment or to inquire about the operation status of the factory. In this paper, we designed and implemented RESTful API for data sharing between factory equipment and monitoring server in Smart Factory. In order to verify the designed API, a testbed was operated for an actual plastics manufacturing plant. As a result, it was confirmed that the testbed can be operated normally in actual operating environment.

The rise of the Internet of Things (IoT) devices have greatly influenced many industries and one of them is healthcare where wearable devices started to track all your daily activities for better health monitoring accuracy and even down to tracking daily food intake in some cases. With the amounts of data that are being tracked and shared between from these devices, questions were raised on how to uphold user's data privacy when data is shared between these IoT devices and third party. With the blockchain platforms started to mature since its inception, the technology can be implemented according to a variety of use case scenarios. In this paper, we present a system architecture based on the healthcare system and IoT network by leveraging on multiple blockchain networks as the medium in between that should enable users to have direct authority on data accessibility of their shared data. We provide proof of concept implementation and highlight the results from our testing to show how the efficiency and scalability of the healthcare system improved without having a significant impact on the performance of the Electronic Medical Record (EMR) that mostly affected by the previous solution since these solutions directly connected to a public blockchain network and which resulted in significant delays and high cost of operation when a large amount of data or complicated functions are involved.

Due to the rapid development of computer and digital technologies, those technologies were used in advertising. In order to meet the needs of marketing, a virtual space or character using 3D graphic augmented reality technology was developed and applied to advertisement. This study analyzed the characteristics of augmented reality in terms of interface design and spatial expression of augmented reality through case study. Firstly, we wanted to find the relationship between the types of the augmented reality advertisement and interface effects. Secondly, we wanted to find the spatial expression methods. The sample survey case studies based on 42 articles from 2012 to 2018 using Chinese and Korean site sharing augmented reality advertisement. The results of the case studies were divided into two parts. Firstly, the relationship between augmented reality types and interface effects were found basing on the augmented reality index. It means that the augmented reality index can be illustrated by using the augmented reality type. Simultaneously, the augmented reality type can be illustrated using the interface effect. The second part was that the types of display were used as a spatial representation. And the results showed that mobile display and public place display were the most frequently used.