Modern Surveillance Research Articles

Ethical Implications Of Ai- Powered Predictive Policing: Balancing Public Safety With Privacy Concerns

This paper seeks to discuss how predictive Policing achieved with the aid of AI, is shaking the foundations of crime fighting as well as resource distribution within the police departments. Using big data and computational modeling, these systems predict offenses to help to deploy security resources most effectively. But some concerns include the ethical issues such as; the use of algorithms is biased, violation of privacy, and lack of responsibility. This is because algorithms are but programmed manifestations of past prejudices and actually amplify the injustice done to the minorities. Furthermore, modern surveillance erodes citizens’ rights and freedoms insinuating itself into private lives, whereas, the systems are often overly secretive making the question of accountability and transparency ever more acute. This study explores the monograph on the subject of ethical dilemmas of AI and its relationship with the policing world, especially in context of the role of prediction in policing and its effect on the conflict between citizens’ protection and their rights. Taking the examples of cases and the present-day legislation, it unveils the concepts of bias-free AI and appropriate legal concerns. Unless policymakers, technologists, and civil society, together are able to create regulations that uphold equality, transparency, and respect for human rights, the current state of affairs will remain suboptimal. Lessons on privacy, human rights, and legal protections that have continued to be significant in achieving responsible uses of predictive algorithms.

Physical Adversarial Attacks for Surveillance: A Survey.

Modern automated surveillance techniques are heavily reliant on deep learning methods. Despite the superior performance, these learning systems are inherently vulnerable to adversarial attacks-maliciously crafted inputs that are designed to mislead, or trick, models into making incorrect predictions. An adversary can physically change their appearance by wearing adversarial t-shirts, glasses, or hats or by specific behavior, to potentially avoid various forms of detection, tracking, and recognition of surveillance systems; and obtain unauthorized access to secure properties and assets. This poses a severe threat to the security and safety of modern surveillance systems. This article reviews recent attempts and findings in learning and designing physical adversarial attacks for surveillance applications. In particular, we propose a framework to analyze physical adversarial attacks and provide a comprehensive survey of physical adversarial attacks on four key surveillance tasks: detection, identification, tracking, and action recognition under this framework. Furthermore, we review and analyze strategies to defend against physical adversarial attacks and the methods for evaluating the strengths of the defense. The insights in this article present an important step in building resilience within surveillance systems to physical adversarial attacks.

Electrochromic Covalent Organic Framework-Assembled Nanofibrous Membranes with Mimetic Chameleon Skin Architectures for Visible and Near-Infrared Camouflage Stealth.

The developments of modern surveillance technology pose great challenges to combat concealment for warfighters. Traditional camouflage suits cannot accommodate the need for camouflage stealth in complex warfare scenarios. Herein, a bidirectional diffusion-controlled in situ synthesis methodology is reported to achieve electrochromic nanofibrous membranes with mimetic chameleon skin structures (CSENs) by assembling electrochromic covalent organic frameworks on nanofibers. CSENs exhibit reversible color changes in the visible and near-infrared ranges under an applied potential with fast response times (25.8 s/26.2 s). The macro- and mesoporous structures in CSENs favored the transportation of electrolyte ions, achieving excellent color difference and coloration efficiency of 35.58 and 1053.26 cm2/C, respectively. Importantly, CSENs feature unique properties of self-standing, breathability, and flexibility, which are attributed to the micrometer pores constructed by entangled nanofibers. As a proof-of-concept study, the CSEN-based flexible electrochromic suit exhibits a dynamic camouflage function in real environments, showing promising properties as smart textiles for dynamic camouflage stealth.

Principles of suspicious activity detection and programs for analyzing suspicious activity using video surveillance cameras

This article delves into the principles of detecting suspicious activity, meticulously examining both logical and psychological facets of human behavior. It places a primary focus on diverse programs for analyzing suspicious activity using video surveillance cameras. A comprehensive overview is provided for key categories of suspicious activity, encompassing crowd clustering, rapid movement, intrusion into secure areas, and unattended items. Within the research framework, the advantages and drawbacks of leading programs for suspicious activity analysis through video surveillance are scrutinized. Furthermore, the article introduces the concept of an innovative program aimed at competing with existing solutions. This software aspires to amalgamate the strengths of competitors while avoiding their shortcomings. The research goal is to identify the potential for creating a highly effective software product capable of successfully competing and meeting the requirements of modern surveillance systems.

Discipline and Power in the Digital Age. Critical Reflections from Foucault’s Thought

In the ever-evolving landscape of the digital age, the theories posited by Michel Foucault four decades ago provide an insightful lens through which to view our contemporary technological society. This article underscores the shift from modern reference disciplines, such as biology, political economy, and linguistics, to the emergent domains of cognitive and computer sciences. By exploring the personalization of online user experiences via data collection and behavioral microtargeting, the study highlights the nuances of modern surveillance. This new era of monitoring bears a resemblance to Foucault’s concept of disciplinary power, marked by its subtle yet omnipresent control. In a world where digital oversight by governments and corporations is increasingly prominent, the relevance of Foucault’s ideas becomes significant for deciphering and traversing the intricate landscapes of power and surveillance in the digital age.

A Corrugated Edged Tapered Slot Antenna with Low Cross-Polarization and Moderate Gain for Phased Array Systems

ABSTRACT This paper presents a miniaturized Vivaldi antenna with a scan volume of ± 30° designed for the 6–18 GHz tactical operational band, commonly employed in phased array systems and modern surveillance applications. The proposed antenna offers enhanced radiation characteristics, low cross polarization, broad beam width, and moderate gain. The design presented in this paper follows a step-by-step approach to achieve enhanced performance in terms of multi-octave bandwidth and compact size. Initially, a traditional Vivaldi antenna was designed and examined, with a modified feed structure. Subsequently, corrugated edges were inserted into the antenna, increasing its electrical length and resulting in good impedance matching, especially in the 6–10 GHz frequency band. A prototype model is fabricated and tested to validate its performance against the simulation results. The measured results demonstrate that the return loss (S11) remains below −10 dB across the operating frequency range, achieving a 50% bandwidth, with a gain of 3–7 dB, and improved co-cross polarization of −23 dB at 6 GHz and −26 dB at 18 GHz.

Vigilance, Popular Control and Neighborhood Surveillance in Besieged Paris (1589–1591)

Abstract Surveillance studies often recall that Michel Foucault had identified the health crises of the plagues of the Ancien Régime as precursory moments in the establishment of modern surveillance. Episodes of civil wars are certainly another example. This study takes for object the capital of the kingdom of France at the siege of Paris, in 1589–1591, when Henry III and then Henri IV tried to reduce to their authority in the rebellious city, head of the Ultra-Catholic Ligue. This unprecedented experience of fear and generalized suspicion allows us to study how the usual mechanisms of social control are used for political ends, but also how new surveillance procedures emerge, based on the written word and a desire for rationalization. This article uses a wide range of sources, from municipal decisions to the reports of chroniclers, but relies mainly on the judicial archives of the Parlement and the Hôtel de Ville to draw up a social history as close as possible to ordinary Parisians. Taking a ground-level approach, the study is particularly interested in ordinary agents of urban control and forms of day-to-day and face-to-face surveillance. In this exceptional climate, the routine work of watching others in the neighborhood turned to political surveillance. The key actors were the Militia, whose action relied on the Parisians themselves, considered as “the eyes and ears” of the League authorities to know who the enemies were and to testify of the loyalty of the inhabitants.

Towards a digital zemiology

Surveillance capitalism (Zuboff, 2019) represents an unprecedented shift in modes and asymmetries of power. Operating within and through the digital sphere, this new era of capitalism has drastic implications for our understandings of modern surveillance, corporate power, and social control. The digital, in its diffuse simultaneously online and offline form, represents the spatialisation of control within which the user and the data they produce are commodified and their identities consolidated and dissected into knowable, marketable demographics which, once reassembled, no longer represent the human being once behind them (Hammond, 2016; Brusseau, 2020). The digital represents a new frontier of harm production, as user-generated data is exploited to serve corporate interests and the normalisation of digital surveillance has given way to user apathy and technological reliance, undermining user autonomy and opportunities for resistance, while commodifying not only our identities but the entirety of the human experience. Within this context an opportunity emerges to develop a zemiology informed by the digital context that can confront the deepening harms of technologisation and consider the future of resistance. By interrogating the intersection between developments in digital technology and harm production, this article aims to acknowledge the proliferation of normalised corporate surveillance through a development in capitalism (Zuboff, 2015; 2019), and to outline opportunities for theoretical development presented by the digital context, drawing upon works within zemiology (Pemberton, 2016), surveillance studies (Murakami Wood, 2007; Murakami Wood and Ball, 2013), postphenomenology (Ihde, 1990; Verbeek, 2011; Wood, 2021; 2022), and disconnection studies (Kuntsman and Miyake, 2022), to present an invitation to both a unique theoretical orientation and an emerging field of study: digital zemiology.

From Surveillance to Toutveillance: Evaluating the Social Impacts of Different Monitoring Paradigms

<p class="MsoNormal" style="text-align: justify;">This article explores the evolution and impacts of different monitoring paradigms: traditional surveillance, sousveillance, and toutveillance. Traditional surveillance, characterized by hierarchical observation from an authoritative entity, often results in power imbalances, privacy invasion, and social inequality. In response, sousveillance monitoring by individuals of those in power emerges as a countermeasure, aiming to democratize oversight but still facing legal and ethical challenges. Toutveillance, which promotes mutual monitoring among all parties, is presented as a potential solution to these issues, leveraging technology such as body cameras to enhance transparency, accountability, and reduce discriminatory practices. The article argues that while each paradigm has its merits and limitations, toutveillance offers a more balanced approach to address the complexities of modern surveillance, privacy concerns, and social justice.</p>

Construction of wear-resistant visible-infrared-compatible camouflage film and its spectral control mechanisms

The ceaseless advancement of modern surveillance technologies has underscored an urgent need for the development of stealth materials with multi-spectral camouflage capabilities. Spectral selectivity, achieved through the design of micro-nano structures, can effectively manage conflicting requirements across various light regimes. Nonetheless, creating visible-Infrared (IR)-compatible devices that are both structurally simple and suitable for use as optic windows or coatings remains a formidable challenge. Here, we report a MgF2/Ag/MgF2 visible-IR-compatible film based on Monte Carlo optimization which has high visible transparency, low IR emissivity, ideal wear-resistance, and dynamic tunability. The designed film can maintain high transparency (∼80%) to ensure replication of background without sacrificing low IR emissivity (∼0.3). Notably, the film maintains its spectral selectivity across a wide range of angles and is endowed with the capability for dynamic modality switching between “heating” and “cooling” states under electrical actuation with good stability under long-term service and harsh environment. This film has a maximum temperature characteristic modulation range of 3.5 °C, demonstrating excellent performance in intelligent thermal management. It is also compatible with electrochromic camouflage devices enabling it to cover a broader spectrum of reconnaissance without contradictions. It is an effective way to fulfill the camouflage requirements and smart thermal management across diverse environments, and possesses great potential for scalability and flexibility.

Missingness and algorithmic bias: an example from the United States National Outbreak Reporting System, 2009-2019.

Growing debates about algorithmic bias in public health surveillance lack specific examples. We tested a common assumption that exposure and illness periods coincide and demonstrated how algorithmic bias can arise due to missingness of critical information related to illness and exposure durations. We examined 9407 outbreaks recorded by the United States National Outbreak Reporting System (NORS) from January 1, 2009 through December 31, 2019 and detected algorithmic bias, a systematic over- or under-estimation of foodborne disease outbreak (FBDO) durations due to missing start and end dates. For 7037 (75%) FBDOs with complete date-time information, ~ 60% reported that the exposure period ended before the illness period started. For 2079 (87.7%) FBDOs with missing exposure dates, average illness durations were ~ 5.3 times longer (p < 0.001) than those with complete information, prompting the potential for algorithmic bias. Modern surveillance systems must be equipped with investigative capacities to examine and assess structural data missingness that can lead to bias.

DESIGN AND ANALYSIS ON IMAGE COMPRESSION USING NEURAL NETWORKS

Due to the explosion of video based information proliferating in the world due to the ubiquitous usage of video cameras the amount of video based information that is currently being generated around the world is huge. And due to security purposes it is becoming imperative that these video data needs to be stored in computer memory for an extended period of time for referrals by security agencies. Because of the advancement of imaging technologies that is being used nowadays it is possible to capture extremely detailed high definition images. But it is not physically possible to store all these high-definition images in computer memories for a long time because infrastructure providers will run out of memory. Image compression is a technology which assists us in this regard. Nowadays this technology has moved from image compression to video compression to compression of 3- dimensional videos which is now becoming more and more popular due to the ever increasing usage of Augmented Reality, Virtual Reality and Ec The quantity of image data produced in modern surveillance networks is rising exponentially year on year which necessitated development of novel schemes for reducing the sizes of the images captured by CCTV cameras while not compromising on the image quality increases when the images are decompressed. This paper proposed a novel Deep Neural Network based method of compressing images in which the image accuracy is not lost but the space it occupies in the memory storage of the computes is reduced greatly compared to other image compression schemes, this proposed scheme is best suited for usage in CCTVs and other networks using Internet of Things which record images and videos continuously.

Target Recognition Using Pre-Trained Convolutional Neural Networks and Transfer Learning

In modern surveillance, automatic target recognition (ATR) is a critical challenge, necessitating rapid and precise object identification, especially in military and disaster response scenarios. This research presents a comprehensive framework for ground target classification, focusing on Synthetic Aperture Radar (SAR) imagery. Harnessing the Moving and Stationary Target Acquisition and Recognition (MSTAR) Mixed Targets dataset, this study integrates Convolutional Neural Networks (CNNs) into SAR image analysis. Transfer learning emerges as a key strategy, adapting insights from pre-trained CNNs to the intricacies of target recognition in SAR imagery. This approach outperforms traditional methods, enhancing efficiency amid dataset annotation challenges. Noteworthy CNN architectures including DarkNet-19, DenseNet-121, InceptionV4, ResNet-152, and VGG19 are explored. The ResNet-152 model demonstrates exceptional performance, emerging as a leading contender with a remarkable testing accuracy of 98.56% when trained from scratch. Furthermore, by employing transfer learning, the model’s accuracy may be further enhanced to reach 98.81% on the testing dataset. This research signifies a transformative SAR imagery path guided by pre-trained CNNs and transfer learning. It reshapes ATR’s accuracy and efficiency, pointing to a future where innovation surmounts challenges previously deemed insurmountable.

АНАЛИЗ ПРИНЦИПОВ ОБРАБОТКИ НАВИГАЦИОННОЙ ИНФОРМАЦИИ И ПОСТРОЕНИЯ РАБОЧЕЙ ЗОНЫ МНОГОПОЗИЦИОННОЙ СИСТЕМЫ НАБЛЮДЕНИЯ

The use of modern surveillance equipment in accordance with ICAO recommendations is aimed at improving the efficiency of air transportation, increasing the capacity of airspace and airfields, and improving the safety of flights and ground operations. The introduction in the Russian Federation of a multi-position surveillance system (MPSS) based on automatic dependent surveillance of the broadcast type (ADS-B) is intended to facilitate the implementation of state and regional aviation development programs in terms of creating conditions for improving flight safety, availability and quality of air navigation services for airspace users. Therefore, the analysis of the principles of constructing a working area and processing navigation information in a multi-position surveillance system for improving the accuracy of aircraft position-fixing is an urgent research task. An approach to improving the efficiency of the MPSS operation when processing information in conditions of noise and interference has been considered. Analysis of the results of modeling the proposed algorithm based on the discrete Kalman filter shows high accuracy of estimating the planned coordinates of an aircraft. Specialized software has been developed to automate the process of calculating and constructing working areas of a multi-position surveillance system.

Drawing on the Development Experiences of Infectious Disease Surveillance Systems Around the World.

High-quality infectious disease surveillance systems are foundational to infectious disease prevention and control. Current major infectious disease surveillance systems globally can be categorized as either indicator-based, which are more specific, or event-based, which are more timely. Modern surveillance systems commonly utilize multi-source data, strengthened information sharing, advanced technology, and improved early warning accuracy and sensitivity. International experience may provide valuable insights for China. China's existing infectious disease surveillance systems require urgent enhancements to monitor emerging infectious diseases and improve the integration and learning capabilities of early warning models. Methods such as establishing multi-stage surveillance systems, promoting cross-sectoral and cross-provincial data sharing, applying advanced technologies like artificial intelligence, and cultivating professional talent should be adopted to enhance the development of intelligent and multipoint-triggered infectious disease surveillance systems in China.

Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study.

Influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve. This study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods. We collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend. This study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China. Complementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models.

Methods on COVID-19 Epidemic Curve Estimation During Emergency Based on Baidu Search Engine and ILI Traditional Surveillance in Beijing, China

Surveillance is an essential work on infectious diseases prevention and control. When the pandemic occurred, the inadequacy of traditional surveillance was exposed, but it also provided a valuable opportunity to explore new surveillance methods. This study aimed to estimate the transmission dynamics and epidemic curve of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron BF.7 in Beijing under the emergent situation using Baidu index and influenza-like illness (ILI) surveillance. A novel hybrid model (multiattention bidirectional gated recurrent unit (MABG)–susceptible–exposed–infected–removed (SEIR)) was developed, which leveraged a deep learning algorithm (MABG) to scrutinize the past records of ILI occurrences and the Baidu index of diverse symptoms such as fever, pyrexia, cough, sore throat, anti-fever medicine, and runny nose. By considering the current Baidu index and the correlation between ILI cases and coronavirus disease 2019 (COVID-19) cases, a transmission dynamics model (SEIR) was formulated to estimate the transmission dynamics and epidemic curve of SARS-CoV-2. During the COVID-19 pandemic, when conventional surveillance measures have been suspended temporarily, cases of ILI can serve as a useful indicator for estimating the epidemiological trends of COVID-19. In the specific case of Beijing, it has been ascertained that cumulative infection attack rate surpass 80.25% (95% confidence interval (95% CI): 77.51%–82.99%) since December 17, 2022, with the apex of the outbreak projected to transpire on December 12. The culmination of existing patients is expected to occur three days subsequent to this peak. Effective reproduction number (Rt) represents the average number of secondary infections generated from a single infected individual at a specific point in time during an epidemic, remained below 1 since December 17, 2022. The traditional disease surveillance systems should be complemented with information from modern surveillance data such as online data sources with advanced technical support. Modern surveillance channels should be used primarily in emerging infectious and disease outbreaks. Syndrome surveillance on COVID-19 should be established to following on the epidemic, clinical severity, and medical resource demand.

РАЗРАБОТКА ПРОГРАММНОГО МОДУЛЯ ПРЕОБРАЗОВАНИЯ ПЕРСПЕКТИВЫ НА ВИДЕОИЗОБРАЖЕНИИ ДЛЯ ВЫБОРА И АНАЛИЗА ЭЛЕМЕНТОВ ИЗОБРАЖЕНИЯ

This paper studies a new approach to improve efficiency of modern surveillance and telemetry systems in robotics. Described and ongoing researches are created with OpenCV and PyGame libraries. A main goal of the study is to improve efficiency of human operator while inspecting or diagnosing various emerging situations. As a key solution we propose to introduce target distortions into images received from surveillance cameras. A prototype module has been developed which allows operator to manually change the perspective on video images. The module was tested on the aerial photography images.

Spread of rabies in the Kyiv Oblast during 2020-2022

In Ukraine, there is a prominent level of rabies circulation among domestic and wild carnivores. Consequently, the epizootic situation of rabies in Ukraine requires studying the spread of this infection using modern surveillance tools, which allow for territorial assessment and identification of risk zones during analysis. The purpose of this research was to describe the spatial characteristics of the rabies epizootic in the Kyiv Oblast during 2020-2022, focusing on both wild and domestic animals. Official reporting data were analysed to examine the total number of samples of pathological material (brain tissue) collected from various animal species with suspected rabies. Positive samples were georeferenced and visualized using software to form clusters of rabies cases based on years and types of animals, creating point layers and density mapping of cases. The analysis revealed that in the Kyiv Oblast, 1788 samples of pathological brain material from animals with suspected rabies were examined, of which 237 were positive. The largest number of samples belonged to wild animals (primarily foxes), acting as a reservoir of the infection. The proportion of wild animals accounted for 64.4% of the total examined, but the number of positive samples varied between 1.7% and 5.5%. Compared to domestic animals (cats and dogs), this figure reached up to 30.0% of the total number of samples examined. Other animal species accounted for 3.2% of positive cases among the total examined. Thus, the highest morbidity rate was observed in domestic animals, representing 72.1% of the total identified positive cases. The highest density of rabies cases in the Kyiv Oblast was observed in the southwest and centre. Persistent clusters of rabies cases were in the Bila Tserkva district and the western outskirts of Kyiv. In the western part of Kyiv, the cluster was formed by cases in wild animals, cats, and dogs. In the Bila Tserkva district, the cluster was formed by cases in wild animals, cats, dogs, and cattle. The highest density of rabies cases in wild animals was registered on the western outskirts of Kyiv

Accurate Movement Detection of Artificially Intelligent Security Objects

A number of intelligent applications demand multiple item detection and tracking as vital elements. Although object search links the identified object through a series of frames, object detection pinpoints the thing's location in a scene. Over the last few decades, a wide range of approaches has been created, which may be divided into 2D and stereo-based 3D techniques. When used in limited circumstances, most of these strategies yield trustworthy findings. These limiting presumptions are used to determine the number of complex elements that object detection and tracking naturally entail. Environmental factors, object appearance, flow density, backdrop colour intensity information, the amount of time an object is present in the scene, object occlusion, a scene's maximum number of things, etc., are among the most often held presumptions. In real-time applications, these approaches' dependability is not assured. A modern surveillance system needs reliable object identification and tracking in an open area.