Biblio

Frame deletion forensics has been a major area of video forensics in recent years. The detection effect of current deep neural network-based methods outperforms previous traditional detection methods. Recently, researchers have used residual features as input to the network to detect frame deletion and have achieved promising results. We propose an IReF (Improved Residual Feature) by analyzing the effect of residual features on frame deletion traces. IReF preserves the main motion features and edge information by denoising and enhancing the residual features, making it easier for the network to identify the tampered features. And the sparse noise reduction reduces the storage requirement. Experiments show that under the 2D convolutional neural network, the accuracy of IReF compared with residual features is increased by 3.81 %, and the storage space requirement is reduced by 78%. In the 3D convolutional neural network with video clips as feature input, the accuracy of IReF features is increased by 5.63%, and the inference efficiency is increased by 18%.

Metaverse is becoming the new standard for social networks and 3D virtual worlds when Facebook officially rebranded to Metaverse in October 2021. Many relevant technologies are used in the metaverse to offer 3D immersive and customized experiences at the user’s fingertips. Despite the fact that the metaverse receives a lot of attention and advantages, one of the most pressing concerns for its users is the safety of their digital material and data. As a result of its decentralization, immutability, and transparency, blockchain is a possible alternative. Our goal is to conduct a comprehensive assessment of blockchain systems in the metaverse to properly appreciate its function in the metaverse. To begin with, the paper introduces blockchain and the metaverse and explains why it’s necessary for the metaverse to adopt blockchain technology. Aside from these technological considerations, this article focuses on how blockchain-based approaches for the metaverse may be used from a privacy and security standpoint. There are several technological challenegs that need to be addressed for making the metaverse a reality. The influence of blockchain on important key technologies with in metaverse, such as Artifical Intelligence, big data and the Internet-of-Things (IoT) is also examined. Several prominent initiatives are also shown to demonstrate the importance of blockchain technology in the development of metaverse apps and services. There are many possible possibilities for future development and research in the application of blockchain technology in the metaverse.

With the increasing number of distributed energy sources and the growing demand for free exchange of energy, Energy internet (EI) is confronted with great challenges of persistent connection, stable transmission, real-time interaction, and security. The new definition of metaverse in the EI field is proposed as a potential solution for these challenges by establishing a massive and comprehensive fusion 3D network, which can be considered as the advanced stage of EI. The main characteristics of the metaverse such as reality to virtualization, interaction, persistence, and immersion are introduced. Specifically, we present the key enabling technologies of the metaverse including virtual reality, artificial intelligence, blockchain, and digital twin. Meanwhile, the potential applications are presented from the perspectives of immersive user experience, virtual power station, management, energy trading, new business, device maintenance. Finally, some challenges of metaverse in EI are concluded.

With novel 3D imaging technology based on computed tomography (CT) set to replace the current 2D X-ray systems, airports face the challenge of adequately preparing airport security officers (screeners) through knowledge building. Virtual reality (VR) bears the potential to greatly facilitate this process by allowing learners to experience and engage in immersive virtual scenarios as if they were real. However, while general aspects of immersion have been explored frequently, less is known about the benefits of immersive technology for instructional purposes in practical settings such as airport security.In the present study, we evaluated how different display technologies (2D vs VR) and segmentation (system-paced vs learner-paced) affected screeners' objective and subjective knowledge gain, cognitive load, as well as aspects of motivation and technology acceptance. By employing a 2 x 2 between-subjects design, four experimental groups experienced uniform learning material featuring information about 3D CT technology and its application in airport security: 2D system-paced, 2D learner-paced, VR system-paced, and VR learner-paced. The instructional material was presented as an 11 min multimedia lesson featuring words (i.e., narration, onscreen text) and pictures in dynamic form (i.e., video, animation). Participants of the learner-paced groups were prompted to initialize the next section of the multimedia lesson by pressing a virtual button after short segments of information. Additionally, a control group experiencing no instructional content was included to evaluate the effectiveness of the instructional material. The data was collected at an international airport with screeners having no prior 3D CT experience (n=162).The results show main effects on segmentation for objective learning outcomes (favoring system-paced), germane cognitive load on display technology (supporting 2D). These results contradict the expected benefits of VR and segmentation, respectively. Overall, the present study offers valuable insight on how to implement instructional material for a practical setting.

The rapid development of technology, makes it easier for everyone to exchange information and knowledge. Exchange information via the internet is threatened with security. Security issues, especially the issue of the confidentiality of information content and its authenticity, are vital things that must protect. Peculiarly for agencies that often hold activities that provide certificates in digital form to participants. Digital certificates are digital files conventionally used as proof of participation or a sign of appreciation owned by someone. We need a security technology for certificates as a source of information known as cryptography. This study aims to validate and authenticate digital certificates with digital signatures using SHA-256, DSA, and 3DES. The use of the SHA-256 hash function is in line with the DSA method and the implementation of 3DES which uses 2 private keys so that the security of digital certificate files can be increased. The pixel changes that appear in the MSE calculation have the lowest value of 7.4510 and the highest value of 165.0561 when the file is manipulated, it answers the security of the proposed method is maintained because the only valid file is the original file.

Intelligent Virtual Agents (IVAs) have become ubiquitous in our daily lives, displaying increased complexity of form and function. Initial IVA development efforts provided basic functionality to suit users' needs, typically in work or educational settings, but are now present in numerous contexts in more realistic, complex forms. In this paper, we focus on personalization of embodied human intelligent virtual agents to assist individuals as part of physical training “exergames”.

In this work, the use of an undercover conversational agent, acting as a participative student in a synchronous virtual reality distance learning scenario is proposed to stimulate social interaction between teacher and students. The outcome of an exploratory user study indicated that the undercover conversational agent is capable of fostering interaction, relieving social pressure, and overall leading to a more satisfactory and engaging learning experience without sacrificing learning performance.

Scanning Transmission Electron Microscopy (STEM) offers high-resolution images that are used to quantify the nanoscale atomic structure and composition of materials and biological specimens. In many cases, however, the resolution is limited by the electron beam damage, since in traditional STEM, a focused electron beam scans every location of the sample in a raster fashion. In this paper, we propose a scanning method based on the theory of Compressive Sensing (CS) and subsampling the electron probe locations using a line hop sampling scheme that significantly reduces the electron beam damage. We experimentally validate the feasibility of the proposed method by acquiring real CS-STEM data, and recovering images using a Bayesian dictionary learning approach. We support the proposed method by applying a series of masks to fully-sampled STEM data to simulate the expectation of real CS-STEM. Finally, we perform the real data experimental series using a constrained-dose budget to limit the impact of electron dose upon the results, by ensuring that the total electron count remains constant for each image.

The camera constructed by a megahertz range intensity modulation active light source and a kilo-frame rate range fast camera based on compressive sensing (CS) technique for three-dimensional (3D) image acquisition was proposed in this research.

Today, social communication through the Internet has become more popular and has become a crucial part of our daily life. Naturally, sending and receiving various data through the Internet has also grown a lot. Keeping important data secure in transit has become a challenge for individuals and even organizations. Therefore, the trinity of confidentiality, integrity, and availability will be essential, and encryption will definitely be one of the best solutions to this problem. Of course, for image data, it will not be possible to use conventional encryption methods for various reasons, such as the redundancy of image data, the strong correlation of adj acent pixels, and the large volume of image data. Therefore, special methods were developed for image encryption. Among the prevalent methods for image encryption is the use of DNA sequences as well as chaos signals. In this paper, a cycling 3D chaotic map and DNA sequences are used to present a new method for color image encryption. Several experimental analyses were performed on the proposed method, and the results proved that the presented method is secure and efficient.

The internet has grown increasingly important in everyone's everyday lives due to the availability of numerous web services such as email, cloud storage, video streaming, music streaming, and search engines. On the other hand, attacks by computer programmes such as bots are a common hazard to these internet services. Captcha is a computer program that helps a server-side company determine whether or not a real user is requesting access. Captcha is a security feature that prevents unauthorised access to a user's account by protecting restricted areas from automated programmes, bots, or hackers. Many websites utilise Captcha to prevent spam and other hazardous assaults when visitors log in. However, in recent years, the complexity of Captcha solving has become difficult for humans too, making it less user friendly. To solve this, we propose creating a Captcha that is both simple and engaging for people while also robust enough to protect sensitive data from bots and hackers on the internet. The suggested captcha scheme employs animated artifacts, rotation, and variable fonts as resistance techniques. The proposed captcha technique proves successful against OCR bots with less than 15% accuracy while being easier to solve for human users with more than 98% accuracy.

In recent years, radar automatic target recognition (RATR) technology based on high-resolution range profile (HRRP) has received extensive attention in various fields. However, insufficient data on non-cooperative targets seriously affects recognition performance of this technique. For HRRP target recognition under few-shot condition, we proposed a novel gaussian deep belief network based on model-agnostic meta-learning (GDBN-MAML). In the proposed method, GDBN allowed real-value data to be transmitted over the entire network, which effectively avoided feature loss due to binarization requirements of conventional deep belief network (DBN) for data. In addition, we optimized the initial parameters of GDBN by multi-task learning based on MAML. In this way, the number of training samples required by the model for new recognition tasks could be reduced. We applied the proposed method to the HRRP recognition experiments of 3 types of 3D simulated aircraft models. The experimental results showed that the proposed method had higher recognition accuracy and generalization performance under few-shot condition compared with conventional deep learning methods.

Keystroke dynamics is one solution to enhance the security of password authentication without adding any disruptive handling for users. Industries are looking for more security without impacting too much user experience. Considered as a friction-less solution, keystroke dynamics is a powerful solution to increase trust during user authentication without adding charge to the user. In this paper, we address the problem of user authentication considering the keystroke dynamics modality. We proposed a new approach based on the conversion of behavioral biometrics data (time series) into a 3D image. This transformation process keeps all the characteristics of the behavioral signal. The time series do not receive any filtering operation with this transformation and the method is bijective. This transformation allows us to train images based on convolutional neural networks. We evaluate the performance of the authentication system in terms of Equal Error Rate (EER) on a significant dataset and we show the efficiency of the proposed approach on a multi-instance system.

Internet of Medical Things (IoMT) is a rapidly growing branch of IoT (Internet of Things), which requires special treatment to cyber security due to confidentiality of healthcare data and patient health threat. Healthcare data and automated medical devices might become vulnerable targets of malicious cyber-attacks. While a large number of robotic applications, including medical and healthcare, employ robot operating system (ROS) as their backbone, not enough attention is paid for ROS security. The paper discusses a security of ROS-based swing doors automation in the context of a robotic hospital framework, which should be protected from cyber-attacks.

Human safety has always been the main priority when working near an industrial robot. With the rise of Human-Robot Collaborative environments, physical barriers to avoiding collisions have been disappearing, increasing the risk of accidents and the need for solutions that ensure a safe Human-Robot Collaboration. This paper proposes a safety system that implements Speed and Separation Monitoring (SSM) type of operation. For this, safety zones are defined in the robot's workspace following current standards for industrial collaborative robots. A deep learning-based computer vision system detects, tracks, and estimates the 3D position of operators close to the robot. The robot control system receives the operator's 3D position and generates 3D representations of them in a simulation environment. Depending on the zone where the closest operator was detected, the robot stops or changes its operating speed. Three different operation modes in which the human and robot interact are presented. Results show that the vision-based system can correctly detect and classify in which safety zone an operator is located and that the different proposed operation modes ensure that the robot's reaction and stop time are within the required time limits to guarantee safety.

In control systems, the operation of the system after an incident occurs is important. This paper proposes to design a whitelist model that can detect anomalies and identify locations of anomalous actuators using finite automata during multiple actuators attack. By applying this model and comparing the whitelist model with the operation data, the monitoring system detects anomalies and identifies anomaly locations of actuator that deviate from normal operation. We propose to construct a whitelist model focusing on the order of the control system operation using binary search trees, which can grasp the state of the system when anomalies occur. We also apply combinatorial compression based on BDD (Binary Decision Diagram) to the model to speed up querying and identification of abnormalities. Based on the model designed in this study, we aim to construct a secured control system that selects and executes an appropriate fallback operation based on the state of the system when anomaly is detected.

3D reconstruction has piqued the interest of many disciplines, and many researchers have spent the last decade striving to improve on latest automated three-dimensional reconstruction systems. Three Dimensional models can be utilized to tackle a wide range of visualization problems as well as other activities. In this paper, we have implemented a method of Digital Surface Map (DSM) generation from Aerial images using Conditional Generative Adversarial Networks (c-GAN). We have used Seg-net architecture of Convolutional Neural Network (CNN) to segment the aerial images and then the U-net generator of c-GAN generates final DSM. The dataset we used is ISPRS Potsdam-Vaihingen dataset. We also review different stages if 3D reconstruction and how Deep learning is now being widely used to enhance the process of 3D data generation. We provide binary cross entropy loss function graph to demonstrate stability of GAN and CNN. The purpose of our approach is to solve problem of DSM generation using Deep learning techniques. We put forth our method against other latest methods of DSM generation such as Semi-global Matching (SGM) and infer the pros and cons of our approach. Finally, we suggest improvements in our methods that might be useful in increasing the accuracy.

This paper proposes 2 multiple layer message security schemes. Information security is carried out through the implementation of cryptography, steganography and image processing techniques. In both schemes, the sensitive data is first encrypted by employing a chaotic function. In the first proposed scheme, LSB steganography is then applied to 2D slices of a 3D image. In the second proposed scheme, a corner detection filter is first applied to the 2D slices of a 3D image, then LSB embedding is carried out in those corner-detected pixels. The number of neighboring pixels used for corner detection is varied and its effect is noted. Performance of the proposed schemes is numerically evaluated using a number of metrics, including the mean squared error (MSE), the peak signal to noise ratio (PSNR), the structure similarity index measure (SSIM), the normalized cross-correlation (NCC), the image fidelity (IF), as well as the image difference (ID). The proposed schemes exhibit superior payload capacity and security in comparison to their counterparts from the literature.

Industrial Control Systems (ICS) are complex systems made up of many components with different tasks. For a safe and secure operation, each device needs to carry out its tasks correctly. To monitor a system and ensure the correct behavior of systems, anomaly detection is used.Models of expected behavior often rely only on cyber or physical features for anomaly detection. We propose an anomaly detection system that combines both types of features to create a dynamic fingerprint of an ICS. We present how a cyber-physical anomaly detection using sound on the physical layer can be designed, and which challenges need to be overcome for a successful implementation. We perform an initial evaluation for identifying actions of a 3D printer.

Traditional passive defense methods cannot resist the constantly updated and evolving cyber attacks. The concept of resilience is introducing to measure the ability of the system to maintain its function under attack. It matters in evaluating the security of modern industrial systems. This paper presents a 3D Resilience State Space method to assess Communication-based train control (CBTC) system resilience under malware attack. We model the spread of malware as two functions: the communicability function \$f\$(x) and the susceptibility function 9 (x). We describe the characteristics of these two function in the CBTC complex network by using the percolation theory. Then we use a perturbation formalism to analyze the impact of malware attack on information flow and use it as an indicator of the cyber layer state. The CBTC cyber-physical system resilience metric formalizes as the system state transitions in three-dimensional state space. The three dimensions respectively represent the cyber layer state, the physical layer state, and the transmission layer state. The simulation results reveal that the proposed framework can effectively assess the resilience of the CBTC system. And the anti-malware programs can prevent the spread of malware and improve CBTC system resilience.

Generative Adversarial Networks (GANs) are machine learning methods that are used in many important and novel applications. For example, in imaging science, GANs are effectively utilized in generating image datasets, photographs of human faces, image and video captioning, image-to-image translation, text-to-image translation, video prediction, and 3D object generation to name a few. In this paper, we discuss how GANs can be used to create an artificial world. More specifically, we discuss how GANs help to describe an image utilizing image/video captioning methods and how to translate the image to a new image using image-to-image translation frameworks in a theme we desire. We articulate how GANs impact creating a customized world.

Teaching evaluation is an indispensable key link in the modern education model. Its purpose is to promote learners' cognitive and non-cognitive development, especially emotional development. However, today's education has increasingly neglected the emotional process of learners' learning. Therefore, a method of using machines to analyze the emotional changes of learners during learning has been proposed. At present, most of the existing emotion recognition algorithms use the extraction of two-dimensional facial features from images to perform emotion prediction. Through research, it is found that the recognition rate of 2D facial feature extraction is not optimal, so this paper proposes an effective the algorithm obtains a single two-dimensional image from the input end and constructs a three-dimensional face model from the output end, thereby using 3D facial information to estimate the continuous emotion of the dimensional space and applying this method to an online learning system. Experimental results show that the algorithm has strong robustness and recognition ability.

The subgraph matching problem arises in a number of modern machine learning applications including segmented images and meshes of 3D objects for pattern recognition, bio-chemical reactions and security applications. This graph-based problem can have a very large and complex solution space especially when the world graph has many more nodes and edges than the template. In a real use-case scenario, analysts may need to query additional information about template nodes or world nodes to reduce the problem size and the solution space. Currently, this query process is done by hand, based on the personal experience of analysts. By analogy to the well-known active learning problem in machine learning classification problems, we present a machine-based active learning problem for the subgraph match problem in which the machine suggests optimal template target nodes that would be most likely to reduce the solution space when it is otherwise overly large and complex. The humans in the loop can then include additional information about those target nodes. We present some case studies for both synthetic and real world datasets for multichannel subgraph matching.

To meet the application need of dynamic visualization VR display of 3D time-varying field, this paper designed an immersive visualization VR system of 3D time-varying field based on the Unity 3D framework. To reduce visual confusion caused by 3D time-varying field flow line drawing and improve the quality and efficiency of visualization rendering drawing, deep learning was used to extract features from the mesoscale vortex of the 3D time-varying field. Moreover, the 3D flow line dynamic visualization drawing was implemented through the Unity Visual Effect Graph particle system.

The project uses 3D immersive technology to innovatively apply virtual reality technology to the monitoring field, and proposes the concept and technical route of remote 3D immersive intelligent control. A design scheme of a three-dimensional immersive remote somatosensory intelligent controller is proposed, which is applied to the remote three-dimensional immersive control of a crawler mobile robot, and the test and analysis of the principle prototype are completed.