A review of deep learning-based detection methods for COVID-19

COVID-19 is a fast-spreading pandemic, and early detection is crucial for stopping the spread of infection. Lung images are used in the detection of coronavirus infection. Chest X-ray (CXR) and computed tomography (CT) images are available for the detection of COVID-19. Deep learning methods have been proven efficient and better performing in many computer vision and medical imaging applications. In the rise of the COVID pandemic, researchers are using deep learning methods to detect coronavirus infection in lung images. In this paper, the currently available deep learning methods that are used to detect coronavirus infection in lung images are surveyed. The available methodologies, public datasets, datasets that are used by each method and evaluation metrics are summarized in this paper to help future researchers. The evaluation metrics that are used by the methods are comprehensively compared.Scopu

End-to-end image steganography using deep convolutional autoencoders

Image steganography is used to hide a secret image inside a cover image in plain sight. Traditionally, the secret data is converted into binary bits and the cover image is manipulated statistically to embed the secret binary bits. Overloading the cover image may lead to distortions and the secret information may become visible. Hence the hiding capacity of the traditional methods are limited. In this paper, a light-weight yet simple deep convolutional autoencoder architecture is proposed to embed a secret image inside a cover image as well as to extract the embedded secret image from the stego image. The proposed method is evaluated using three datasets - COCO, CelebA and ImageNet. Peak Signal-to-Noise Ratio, hiding capacity and imperceptibility results on the test set are used to measure the performance. The proposed method has been evaluated using various images including Lena, airplane, baboon and peppers and compared against other traditional image steganography methods. The experimental results have demonstrated that the proposed method has higher hiding capacity, security and robustness, and imperceptibility performances than other deep learning image steganography methods

Accessible Cultural Heritage through Explainable Artificial Intelligence

International audienceEthics Guidelines for Trustworthy AI advocate for AI technology that is, among other things, more inclusive. Explainable AI (XAI) aims at making state of the art opaque models more transparent, and defends AI-based outcomes endorsed with a rationale explanation, i.e., an explanation that has as target the non-technical users. XAI and Responsible AI principles defend the fact that the audience expertise should be included in the evaluation of explainable AI systems. However, AI has not yet reached all public and audiences , some of which may need it the most. One example of domain where accessibility has not much been influenced by the latest AI advances is cultural heritage. We propose including minorities as special user and evaluator of the latest XAI techniques. In order to define catalytic scenarios for collaboration and improved user experience, we pose some challenges and research questions yet to address by the latest AI models likely to be involved in such synergy

LFR face dataset:Left-Front-Right dataset for pose-invariant face recognition in the wild

In this work, a new multitask convolutional neural network (CNN) is proposed aiming for the recognition of face under pose variations. Furthermore, the combination of pose estimation for each corresponding pose in a separate fashion allows robust face recognition in presence of various facial expressions as well as low illuminations. First, a CNN model for pose estimation is proposed. The pose estimation model is trained using a self-collected dataset built from three popular datasets including FLW, CEP, and CASIA-WebFace using three categories of face image capture such as Left side, Frontal and right side. Experimental evaluation has been conducted using two datasets: Pointing'04 and Schneiderman. Results reveal the robustness of the proposed pose estimation model. Moreover, the proposed face pose estimation is applied on three datasets to widen the dataset and make it bigger for training and testing deep learning models.identity. Compared with the recent and related techniques, the proposed system has been shown to outperform related works and deliver state-of-the-art performance for pose estimation. The built large-scale dataset for training may improve the accuracy of the proposed system as increasing the number of images for each identity will cover a wider range of variations of the face. Also, the generated dataset can be improved in terms of the number of identities using the same methodology on other datasets like VGGFace2. Acknowledgment This publication was made by NPRP grant # NPRP8-140-2-065 from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

An image steganography approach based on k-least significant bits (k-LSB)

Image steganography is the operation of hiding a message into a cover image. the message can be text, codes, or image. Hiding an image into another is the proposed approach in this paper. Based on LSB coding, a k-LSB-based method is proposed using k least bits to hide the image. For decoding the hidden image, a region detection operation is used to know the blocks contains the hidden image. The resolution of stego image can be affected, for that, an image quality enhancement method is used to enhance the image resolution. To demonstrate the effectiveness of the proposed approach, we compare it with some of the state-of-the-art methods.This publication was made by NPRP grant # NPRP 11S - 0113 - 180276 from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

A review of video surveillance systems

Automated surveillance systems observe the environment utilizing cameras. The observed scenario is then analysed using motion detection, crowd behaviour, individual behaviour, interaction between individuals, crowds and their surrounding environment. These automatic systems accomplish multitude of tasks which include, detection, interpretation, understanding, recording and creating alarms based on the analysis. Till recent, studies have achieved enhanced monitoring performance along with avoiding possible human failures by manipulation of different features of these systems. This paper presents a comprehensive review of such video surveillance systems as well as the components used with them. The description of the architectures used is presented which follows the most required analyses in these systems. For the bigger picture and wholesome view of the system, existing surveillance systems were compared in terms of characteristics, advantages, and difficulties which are tabulated in this paper. Adding to this, future trends are discussed which charts a path into the upcoming research directions.This publication was made by NPRP grant # NPRP8-140?2-065 from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Pose-invariant face recognition with multitask cascade networks

In this work, a face recognition method is proposed for face under pose variations using a multitask convolutional neural network (CNN). Furthermore, a pose estimation method followed by a face identification module is combined in a cascaded structure and used separately. In the presence of various facial expressions as well as low illuminations, datasets that include separated face poses can enhance the robustness of face recognition. The proposed method relies on a pose estimation module using a convolutional neural network model and trained on three categories of face image capture such as the left side, frontal, and right side. Second, three CNN models are used for face identification according to the estimated pose. The Left-CNN model, Front-CNN model, and Right-CNN model are used to identify the face for the left, frontal, and right pose of the face, respectively. Because face images may contain some useless information (e.g., background content), we propose a skin-based face segmentation method using structure texture decomposition and the color-invariant descriptor. Experimental evaluation has been conducted using the proposed cascade-based face recognition system that consists of the aforementioned steps (i.e., pose estimation, face segmentation, and face identification) and is assessed on four different datasets and its superiority has been shown over related state-of-the-art techniques. Results reveal the contribution of the separate representation, skin segmentation, and pose estimation in the recognition robustness.This publication was made by NPRP Grant # NPRP8-140-2-065 from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

BEMD-3DCNN-based method for COVID-19 detection

The coronavirus outbreak continues to spread around the world and no one knows when it will stop. Therefore, from the first day of the identification of the virus in Wuhan, China, scientists have launched numerous research projects to understand the nature of the virus, how to detect it, and search for the most effective medicine to help and protect patients. Importantly, a rapid diagnostic and detection system is a priority and should be developed to stop COVID-19 from spreading. Medical imaging techniques have been used for this purpose. Current research is focused on exploiting different backbones like VGG, ResNet, DenseNet, or combining them to detect COVID-19. By using these backbones many aspects cannot be analyzed like the spatial and contextual information in the images, although this information can be useful for more robust detection performance. In this paper, we used 3D representation of the data as input for the proposed 3DCNN-based deep learning model. The process includes using the Bi-dimensional Empirical Mode Decomposition (BEMD) technique to decompose the original image into IMFs, and then building a video of these IMF images. The formed video is used as input for the 3DCNN model to classify and detect the COVID-19 virus. The 3DCNN model consists of a 3D VGG-16 backbone followed by a Context-aware attention (CAA) module, and then fully connected layers for classification. Each CAA module takes the feature maps of different blocks of the backbone, which allows learning from different feature maps. In our experiments, we used 6484 X-ray images, of which 1802 were COVID-19 positive cases, 1910 normal cases, and 2772 pneumonia cases. The experiment results showed that our proposed technique achieved the desired results on the selected dataset. Additionally, the use of the 3DCNN model with contextual information processing exploited CAA networks to achieve better performance.This publication was supported by a Qatar University COVID-19 Emergency Response Grant ( QUERG-CENG-2020-1 ). The findings achieved herein are solely the responsibility of the authors. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of Qatar University.Scopu

Face-Fake-Net: The Deep Learning Method for Image Face Anti-Spoofing Detection : 45

Due to the increasingly growing demand for user identification on cell phones, PCs, laptops, and so on, face anti-spoofing has risen to significance and is an active research area in academia and industry. The detection of the real face then recognize it present an important challenge regarding the techniques that can be used to spoof any recognition system like masks, printed photos. This paper we present an anti-spoofing face method to solve the real-world scenario that learns the target domain classifier based on samples used for training in a particular source domain. Specifically, with the conventional regression CNN, the Spatial/Channel-wise Attention Modules were introduced. Two modules, namely the Spatial-wise Attention Module and the Channel-wise Attention Module, were used at spatial and channel levels to improve local features and ignore the irrelevant features. Extensive experiments on current collections with benchmarks datasets verifies that the recommended solution will significantly benefit from the two modules and better generalization capability by providing significantly improved results in anti-spoofing.This work was supported by the NPRP from the Qatar National Research Fund (a member of the Qatar Foundation) under the National Priorities Research Program (NPRP) under Grant PRP12S-0312-190332. The statements made herein are solely the responsibility of the authors.Scopu

Image Steganography: A Review of the Recent Advances

Image Steganography is the process of hiding information which can be text, image or video inside a cover image. The secret information is hidden in a way that it not visible to the human eyes. Deep learning technology, which has emerged as a powerful tool in various applications including image steganography, has received increased attention recently. The main goal of this paper is to explore and discuss various deep learning methods available in image steganography field. Deep learning techniques used for image steganography can be broadly divided into three categories - traditional methods, Convolutional Neural Network-based and General Adversarial Network-based methods. Along with the methodology, an elaborate summary on the datasets used, experimental set-ups considered and the evaluation metrics commonly used are described in this paper. A table summarizing all the details are also provided for easy reference. This paper aims to help the fellow researchers by compiling the current trends, challenges and some future direction in this field.This work was supported by the Qatar National Research Fund (a member of Qatar Foundation) under Grant NPRP11S-0113-180276. Open Access funding provided by the Qatar National Library.Scopu