Biblio

In this work, we have proposed a state-of-the-art face logging system that detects and logs high quality cropped face images of the people in real-time for security applications. Multiple strategies based on resolution, velocity and symmetry of faces have been applied to obtain best quality face images. The proposed system handles the issue of motion blur in the face images by determining the velocities of the detections. The output of the system is the face database, where four faces for each detected person are stored along with the time stamp and ID number tagged to it. The facial features are extracted by our system, which are used to search the person-of-interest instantly. The proposed system has been implemented in a docker container environment on two edge devices: the powerful NVIDIA Jetson TX2 and the cheaper NVIDIA Jetson N ano. The light and fast face detector (LFFD) used for detection, and ResN et50 used for facial feature extraction are optimized using TensorRT over these edge devices. In our experiments, the proposed system achieves the True Acceptance Rate (TAR) of 0.94 at False Acceptance Rate (FAR) of 0.01 while detecting the faces at 20–30 FPS on NVIDIA Jetson TX2 and about 8–10 FPS on NVIDIA Jetson N ano device. The advantage of our system is that it is easily deployable at multiple locations and also scalable based on application requirement. Thus it provides a realistic solution to face logging application as the query or suspect can be searched instantly, which may not only help in investigation of incidents but also in prevention of untoward incidents.

Due to immense need for implementing security measures and control ongoing activities, intelligent video analytics is regarded as one of the outstanding and challenging research domains in Computer Vision. Assigning video operator to manually monitor the surveillance videos 24×7 to identify occurrence of interesting and anomalous events like robberies, wrong U-turns, violence, accidents is cumbersome and error- prone. Therefore, to address the issue of continuously monitoring surveillance videos and detect the anomalies from them, a deep learning approach based on pipelined sequence of convolutional autoencoder and sequence to sequence long short-term memory autoencoder has been proposed. Specifically, unsupervised learning approach encompassing one-class classification paradigm has been proposed for detection of anomalies in videos. The effectiveness of the propped model is demonstrated on benchmarked anomaly detection dataset and significant results in terms of equal error rate, area under curve and time required for detection have been achieved.

Radar sensors have shown great potential for surveillance and security authentication applications. However, a thorough analysis of their vulnerability to spoofing or replay attacks has not been performed yet. In this paper, the feasibility of performing spoofing attacks to radar sensor is studied and experimentally verified. First, a simple binary phase-shift keying system was used to generate artificial spectral components in the radar's demodulated signal. Additionally, an analog phase shifter was driven by an arbitrary signal generator to mimic the human cardio-respiratory motion. Characteristic time and frequency domain cardio-respiratory human signatures were successfully generated, which opens possibilities to perform spoofing attacks to surveillance and security continuous authentication systems based on microwave radar sensors.

Cyber-attacks toward cyber-physical systems are one of the main concerns of smart grid's operators. However, many of these cyber-attacks, are toward unmanned substations where the cyber-attackers needs to be close enough to substation to malfunction protection and control systems in substations, using Electromagnetic signals. Therefore, in this paper, a new threat detection algorithm is proposed to prevent possible cyber-attacks toward unmanned substations. Using surveillance camera's streams and based on You Only Look Once (YOLO) V3, suspicious objects in the image are detected. Then, using Intersection over Union (IOU) and Generalized Intersection Over Union (GIOU), threat distance is estimated. Finally, the estimated threats are categorized into three categories using color codes red, orange and green. The deep network used for detection consists of 106 convolutional layers and three output prediction with different resolutions for different distances. The pre-trained network is transferred from Darknet-53 weights trained on 80 classes.

This paper proposes an artificial immune information security protection technology architecture for embedded system of Electric power equipment. By simulating the three functions of human immunity, namely "immune homeostasis", "immune surveillance" and "immune defense", the power equipment is endowed with the ability of human like active immune security protection. Among them, "immune homeostasis" is constructed by trusted computing technology components to establish a trusted embedded system running environment. Through fault-tolerant component construction, "immune surveillance" and "immune defense" realize illegal data defense, business logic legitimacy check and equipment status evaluation, realize real-time perception and evaluation of power equipment's own security status, as well as fault emergency handling and event backtracking record, so that power equipment can realize self recovery from abnormal status. The proposed technology architecture is systematic, scientific and rich in scalability, which can significantly improve the information security protection ability of electric power equipment.

This paper exploits the possibility of exposing the location of active eavesdropper in commodity passive RFID system. Such active eavesdropper can activate the commodity passive RFID tags to achieve data eavesdropping and jamming. In this paper, we show that these active eavesdroppers can be significantly detrimental to the commodity passive RFID system on RFID data security and system feasibility. We believe that the best way to defeat the active eavesdropper in the commodity passive RFID system is to expose the location of the active eavesdropper and kick it out. To do so, we need to localize the active eavesdropper. However, we cannot extract the channel from the active eavesdropper, since we do not know what the active eavesdropper's transmission and the interference from the tag's backscattered signals. So, we propose an approach to mitigate the tag's interference and cancel out the active eavesdropper's transmission to obtain the subtraction-and-division features, which will be used as the input of the machine learning model to predict the location of active eavesdropper. Our preliminary results show the average accuracy of 96% for predicting the active eavesdropper's position in four grids of the surveillance plane.

Human-machine teaming (HMT) operates in a context defined by the mission. Varying from the complexity and disturbance in the cooperation between humans and machines, a single machine has difficulty handling work with humans in the scales of efficiency and workload. Swarm of machines provides a more feasible solution in such a mission. Human-swarm teaming (HST) extends the concept of HMT in the mission, such as persistent surveillance, search-and-rescue, warfare. Bringing the concept of HST faces several scientific challenges. For example, the strategies of allocation on the high-level decision making. Here, human usually plays the supervisory or decision making role. Performance of such fixed structure of HST in actual mission operation could be affected by the supervisor’s status from many aspects, which could be considered in three general parts: workload, situational awareness, and trust towards the robot swarm teammate and mission performance. Besides, the complexity of a single human operator in accessing multiple machine agents increases the work burdens. An interface between swarm teammates and human operators to simplify the interaction process is desired in the HST.In this paper, instead of purely considering the workload of human teammates, we propose the computational model of human swarm interaction (HSI) in the simulated map surveillance mission. UAV swarm and human supervisor are both assigned in searching a predefined area of interest (AOI). The workload allocation of map monitoring is adjusted based on the status of the human worker and swarm teammate. Workload, situation awareness ability, trust are formulated as independent models, which affect each other. A communication-aware UAV swarm persistent surveillance algorithm is assigned in the swarm autonomy portion. With the different surveillance task loads, the swarm agent’s thrust parameter adjusts the autonomy level to fit the human operator’s needs. Reinforcement learning is applied in seeking the relative balance of workload in both human and swarm sides. Metrics such as mission accomplishment rate, human supervisor performance, mission performance of UAV swarm are evaluated in the end. The simulation results show that the algorithm could learn the human-machine trust interaction to seek the workload balance to reach better mission execution performance. This work inspires us to leverage a more comprehensive HST model in more practical HMT application scenarios.

Surveillance systems involve a camera module (at a fixed location) connected/streaming video via Internet Protocol to a (video) server. In our IMPRINT consortium project, by mounting miniaturised camera module/s on mobile quadruped-lizard like robots, we developed a stealth surveillance system, which could be very useful as a monitoring system in hostage situations. In this paper, we report about the communication system that enables secure transmission of: Live-video from robots to a server, GPS-coordinates of robots to the server and Navigation-commands from server to robots. Since the end application is for stealth surveillance, often can involve sensitive data, data security is a crucial concern, especially when data is transmitted through the internet. We use the RC4 algorithm for video transmission; while the AES algorithm is used for GPS data and other commands’ data transmission. Advantages of the developed system is easy to use for its web interface which is provided on the control station. This communication system, because of its internet-based communication, it is compatible with any operating system environment. The lightweight program runs on the control station (on the server side) and robot body that leads to less memory consumption and faster processing. An important requirement in such hostage surveillance systems is fast data processing and data-transmission rate. We have implemented this communication systems with a single-board computer having GPU that performs better in terms of speed of transmission and processing of data.

Security is a major thing to focus on during this modern era as it is very important to secure your surroundings for the well being of oneself and his family, But there are many drawbacks of using conventional security surveillance cameras as they have to be set in a particular angle for good visual and they do not cover a large area, conventional security cameras can only be used from a particular device and cannot alert the user during an unforeseen circumstance. Hence we require a much more efficient device for better security a web controlled surveillance robot is much more practical device to be used compared to conventional security surveillance, this system needs a single camera to perform its operation and the user can monitor a wide range of area, any device with a wireless connection to the internet can be used to operate this device. This robot can move to any location within the range of the network and can be accessed globally from anywhere and as it uses only one camera to secure a large area it is also cost-efficient. At the core of the system lies Raspberry-pi which is responsible for all the operation of the system and the size of the device can be engineered according to the area it is to be used.

As data being produced by IoT applications continues to explode, there is a growing need to bring computing power closer to the source of the data to meet the response-time, power dissipation and cost goals of performance-critical applications in various domains like Industrial Internet of Things (IIoT), Automated Driving, Medical Imaging or Surveillance among others. This paper proposes a data collection and utilization framework that allows runtime platform and application data to be sent to an edge and cloud system via data collection agents running close to the platform. Agents are connected to a cloud system able to train AI models to improve overall energy efficiency of an AI application executed on a edge platform. In the implementation part we show the benefits of FPGA-based platform for the task of object detection. Furthermore we show that it is feasible to collect relevant data from an FPGA platform, transmit the data to a cloud system for processing and receiving feedback actions to execute an edge AI application energy efficiently. As future work we foresee the possibility to train, deploy and continuously improve a base model able to efficiently adapt the execution of edge applications.

Nowadays, an increasing trend to use autonomous Unmanned Aerial Vehicles (UAV) for applications like logistics as well as security and surveillance can be recorded. Autonomic UAVs require robust and precise navigation to ensure efficient and safe operation even in strong multipath environments and (intended) interference. The need for robust navigation on UAVs implies the necessary integration of low-cost, lightweight, and compact array antennas as well as structures for multipath mitigation into the UAV platform. This article investigates a miniaturized antenna array mounted on top of vertical choke rings for robust navigation purposes. The array employs four 3D printed elements based on dielectric resonators capable of operating in all GNSS bands while compact enough for mobile applications such as UAV.

Wireless sensor networks (WSNs) are underlying network infrastructure for a variety of surveillance applications. The network should be tolerant of unexpected failures of sensor nodes to meet the Quality of Service (QoS) requirements of these applications. One major cause of failure is active security attacks such as Depletion-of-Battery (DoB) attacks. This paper model the problem of detecting such attacks as an anomaly detection problem in a dynamic graph. The problem is addressed by employing a cluster ensemble approach called the K-Means Spectral and Hierarchical ensemble (KSH) approach. The experimental result shows that KSH detected DoB attacks with better accuracy when compared to baseline approaches.

Sound plays a key role in human life and therefore sound recognition system has a great future ahead. Sound classification and identification system has many applications such as system for personal security, critical surveillance, etc. The main aim of this paper is to detect and classify the security sound event using the surveillance camera systems with integrated microphone based on the generated spectrograms of the sounds. This will enable to track security events in cases of emergencies. The goal is to propose a security system to accurately detect sound events and make a better security sound event detection system. We propose to use a convolutional neural network (CNN) to design the security sound detection system to detect a security event with minimal sound. We used the spectrogram images to train the CNN. The neural network was trained using different security sounds data which was then used to detect security sound events during testing phase. We used two datasets for our experiment training and testing datasets. Both the datasets contain 3 different sound events (glass break, gun shots and smoke alarms) to train and test the model, respectively. The proposed system yields the good accuracy for the sound event detection even with minimum available sound data. The designed system achieved accuracy was 92% and 90% using CNN on training dataset and testing dataset. We conclude that the proposed sound classification framework which using the spectrogram images of sounds can be used efficiently to develop the sound classification and recognition systems.

The research proposal discloses a novel drone-based ad-hoc network that leverages acoustic information for power plant surveillance and utilizes a secure blockchain model for protecting the integrity of drone communication over the network. The paper presents a vision for the drone-based networks, wherein drones are employed for monitoring the complex power plant machinery. The drones record acoustic information generated by the power plants and detect anomalies or deviations in machine behavior based on collected acoustic data. The drones are linked to distributed network of computing devices in possession with the plant stakeholders, wherein each computing device maintains a chain of data blocks. The chain of data blocks represents one or more transactions associated with power plants, wherein transactions are related to high risk auditory data set accessed by the drones in an event of anomaly or machine failure. The computing devices add at least one data block to the chain of data blocks in response to valid transaction data, wherein the transaction data is validated by the computing devices owned by power plant personnel.

With the development of technology, application of the Internet in daily life is increasing, making our connection with the Internet closer. However, with the improvement of convenience, information security has become more and more important. How to ensure information security in a convenient living environment is a question worth discussing. For instance, the widespread deployment of IP-cameras has made great progress in terms of convenience. On the contrary, it increases the risk of privacy exposure. Poorly designed surveillance devices may be implanted with suspicious software, which might be a thorny issue to human life. To effectively identify vulnerable devices, we design an SDN-based identification system that uses machine learning technology to identify brands and probable model types by identifying packet features. The identifying results make it possible for further vulnerability analysis.

Face capturing is a task to capture and store the "best" face of each person passing by the monitor. To some extent, it is similar to face tracking, but uses a different criterion and requires a valuable (i.e., high-quality and recognizable) face selection procedure. Face capturing systems play a critical role in public security. When deployed on edge devices, it is capable of reducing redundant storage in data center and speeding up retrieval of a certain person. However, high computation complexity and high repetition rate caused by ID switch errors are major challenges. In this paper, we propose a novel solution to constructing a real-time low-repetition face capturing system on chips. First, we propose a two-stage association algorithm for memory-efficient and accurate face tracking. Second, we propose a fast and reliable face quality estimation algorithm for valuable face selection. Our pipeline runs at over 20fps on Hisiv 3559A SoC with a single NNIE device for neural network inference, while achieving over 95% recall and less than 0.4 repetition rate in real world surveillance videos.

Surveillance systems are widely used in airports, streets, banks, military areas, borders, hospitals, and schools. There are two types of surveillance systems which are real-time systems and offline surveillance systems. Usually, security people track videos on time in monitoring rooms to find out abnormal human activities. Real-time human tracking from videos is very expensive especially in airports, borders, and streets due to the huge number of surveillance cameras. There are a lot of research works have been done for automated surveillance systems. In this paper, we presented a new surveillance system to recognize human activities from several cameras using machine learning algorithms. Sequences of images are collected from cameras using the internet of things technology from indoor or outdoor areas. A feature vector is created for each recognized moving object, then machine learning algorithms are applied to extract moving object activities. The proposed abnormal event detection system gives very promising results which are more than 96% accuracy in Multilayer Perceptron, Iterative Classifier Optimizer, and Random Forest algorithms.

Video sharing from closed-circuit television video recording or in social media interaction requires self-authentication for responsible and reliable data sharing. Similarly, surveillance video recording is a powerful method of deterring unlawful activities. A Solution-by-Design can be helpful in terms of making a captured video immutable, as such recordings cannot become a piece of evidence until proven to be unaltered. This paper presents a computationally inexpensive method of preserving a chain-of-evidence in surveillance videos using steganography and hashing. The method conforms to the data protection regulations which are increasingly adopted by governments, and is applicable to network edge storage. Security credentials are stored in a hardware wallet independently of the video capture device itself, while evidential information is stored within video frames themselves, independently of the content. The proposed method has turned out to not only preserve the integrity of the stored video data but also results in very limited degradation of the video data due to steganography. Despite the presence of steganographic information, video frames are still available for common image processing tasks such as tracking and classification.

ATM transactions are verified using two-factor authentication. The PIN is one of the factors (something you know) and the ATM Card is the other factor (something you have). Therefore, banks make significant investments on PIN Mailers and HSMs to preserve the security and confidentiality in the generation, validation, management and the delivery of the PIN to their customers. Moreover, banks install surveillance cameras inside ATM cubicles as a physical security measure to prevent fraud and theft. However, in some cases, ATM PIN-Pad and the PIN entering process get revealed through the surveillance camera footage itself. We demonstrate that visibility of forearm movements is sufficient to infer PINs with a significant level of accuracy. Video footage of the PIN entry process simulated in an experimental setup was analyzed using two approaches. The human observer-based approach shows that a PIN can be guessed with a 30% of accuracy within 3 attempts whilst the computer-assisted analysis of footage gave an accuracy of 50%. The results confirm that ad-hoc installation of surveillance cameras can weaken ATM PIN security significantly by potentially exposing one factor of a two-factor authentication system. Our investigation also revealed that there are no guidelines, standards or regulations governing the placement of surveillance cameras inside ATM cubicles in Sri Lanka.

The use of Automatic Dependent Surveillance - Broadcast (ADS-B) for aircraft tracking and flight management operations is widely used today. However, ADS-B is prone to several cyber-security threats due to the lack of data authentication and encryption. Recently, Blockchain has emerged as new paradigm that can provide promising solutions in decentralized systems. Furthermore, software containers and Microservices facilitate the scaling of Blockchain implementations within cloud computing environment. When fused together, these technologies could help improve Air Traffic Control (ATC) processing of ADS-B data. In this paper, a Blockchain implementation within a Microservices framework for ADS-B data verification is proposed. The aim of this work is to enable data feeds coming from third-party receivers to be processed and correlated with that of the ATC ground station receivers. The proposed framework could mitigate ADS- B security issues of message spoofing and anomalous traffic data. and hence minimize the cost of ATC infrastructure by throughout third-party support.

Blockchain technology is a decentralized ledger of all transactions across peer to peer network. Being decentralized in nature, a blockchain is highly secure as no single user can alter or remove an entry in the blockchain. The security of office premises and data is a very major concern for any organization. This paper majorly focuses on its application of blockchain technology in security surveillance. This paper proposes a blockchain based multi level network model for security surveillance system. The proposed system architecture is composed of different blockchain based systems connected to a multi level decentralized blockchain system to insure authentication, secure storage, Integrity and accountability.

Monitoring for security and well-being in highly populated areas is a critical issue for city administrators, policy makers and urban planners. As an essential part of many dynamic and critical data-driven tasks, situational awareness (SAW) provides decision-makers a deeper insight of the meaning of urban surveillance. Thus, surveillance measures are increasingly needed. However, traditional surveillance platforms are not scalable when more cameras are added to the network. In this work, a smart surveillance as an edge service has been proposed. To accomplish the object detection, identification, and tracking tasks at the edge-fog layers, two novel lightweight algorithms are proposed for detection and tracking respectively. A prototype has been built to validate the feasibility of the idea, and the test results are very encouraging.

The automatic face tracking and detection has been one of the fastest developing areas due to its wide range of application, security and surveillance application in particular. It has been one of the most interest subjects, which suppose but yet to be wholly explored in various research areas due to various distinctive factors: varying ethnic groups, sizes, orientations, poses, occlusions and lighting conditions. The focus of this paper is to propose an improve algorithm to speed up the face tracking and detection process with the simple and efficient proposed novel edge detector to reject the non-face-likes regions, hence reduce the false detection rate in an automatic face tracking and detection in still images with multiple faces for facial expression system. The correct rates of 95.9% on the Haar face detection and proposed novel edge detector, which is higher 6.1% than the primitive integration of Haar and canny edge detector.

Recently, smart video security systems have been active. The existing video security system is mainly a method of detecting a local abnormality of a unit camera. In this case, it is difficult to obtain the characteristics of each local region and the situation for the entire watching area. In this paper, we developed an object map for the entire surveillance area using a combination of surveillance cameras, and developed an algorithm to detect anomalies by learning normal situations. The surveillance camera in each area detects and tracks people and cars, and creates a local object map and transmits it to the server. The surveillance server combines each local maps to generate a global map for entire areas. Probability maps were automatically calculated from the global maps, and normal and abnormal decisions were performed through trained data about normal situations. For three reporting status: normal, caution, and warning, and the caution report performance shows that normal detection 99.99% and abnormal detection 86.6%.

Robots are becoming more and more prevalent in many real world scenarios. Housekeeping, medical aid, human assistance are a few common implementations of robots. Military and Security are also major areas where robotics is being researched and implemented. Robots with the purpose of surveillance in war zones and terrorist scenarios need specific functionalities to perform their tasks with precision and efficiency. In this paper, we present a model of Military Surveillance Robot developed using Robot Operating System. The map generation based on Kinect sensor is presented and some test case scenarios are discussed with results.