Illicit Activities Research Articles

Key Technologies and Typical Applications in Deepfake Detection

As deepfaking technology advances, it increasingly facilitates the seamless splicing of a person's voice, facial expressions, and body movements into source images or videos to create hyper-realistic virtual content. This growing technological sophistication, paired with the open-source nature of neural network algorithms, has unfortunately led to the exploitation of deepfake technology by individuals for illicit activities, seeking financial gain. This situation highlights the urgent necessity to thoroughly understand and vigilantly monitor the evolution of deepfake technology. This paper delves into the fundamental concepts and operational mechanisms underlying contemporary deepfake detection methods. It aims to augment the comprehension of these technologies and assesses the strengths and weaknesses of current detection approaches. This evaluation provides critical insights into their operational efficacy and reliability in differentiating genuine from manipulated content. Furthermore, the paper explores the challenges that current technologies face, such as the rapid evolution of deepfake methods that may outpace detection capabilities. By thoroughly analyzing these technologies, the paper endeavors to offer a comprehensive overview and projects future trends in deepfake detection. It anticipates developments that could significantly enhance the efficacy of countermeasures and thus bolster the fight against digital identity fraud and misinformation, safeguarding digital media integrity in an increasingly synthetic landscape.

The Anti-illicit Activity Regime of the Multilateral Development Banks: Criminal Acts or Prohibited Practices?

The spread of illicit activity across the global economy presents significant challenges to international development. Despite the well-recognised global incidence of corruption, fraud, and money laundering in development-focused investment projects, the responses of the multilateral development banks (MDBs) to these threats remain understudied. Our article offers the first comprehensive study into the comparative historical emergence and evolution of MDB responses to illicit activity. By identifying and analysing critical junctures in this history, we argue that the MDBs have tended to approach illicit activity as prohibited practices rather than criminal acts. We contend that this is an intentional choice made by the MDBs that absolves these organisations from any real responsibility in minimising illicit activity, finding their concern to be ensuring contractual compliance in their lending operations rather than curtailing criminal behaviour and their preference to be resolving contractual deviations in-house as opposed to coordinating with local jurisdictions and law enforcement agencies.

The Evolution of Intelligence Doctrine and Its Contribution to Maritime Security

The interconnected nature of the oceans and the absence of physical barriers along the maritime borders make it tough to control the movement of people and goods. Combating illicit activities at sea poses significant challenges that require cooperation between national security agencies and partner nations. To support decisionmaking in maritime security operations, it is crucial to establish a transparent maritime intelligence architecture based on cooperative techniques, knowledge sharing, and technological resources. Maritime security actors should collaborate to address gaps resulting from limited resources and the evolving modus operandi of nonstate threats. Therefore, a maritime intelligence architecture should outline the interaction between national and foreign agencies involved in collecting, analyzing, and sharing intelligence information. To meet those interoperability requirements, the article shows that the promotion of maritime security worldwide has led to the development of a new maritime intelligence doctrine based on cooperation and information sharing rather than the traditional “compartmentation of information” and “need to know” principles.

A place of encounter: Health and social care workers’ perspectives on public gathering spaces for people using drugs

Background: The use of drugs is an escalating concern, with significant implications for the health and daily functionality for individuals. It has proven challenging to provide accessible health and social services. In the context of addressing substance use issues in urban areas, public gathering places for individuals who use drugs play a multifaceted role, serving as sites of belonging, support and potential illicit activity. This qualitative study explores the experiences of healthcare and social workers regarding a public gathering place, referred to as “the Bench”, in a medium-sized Norwegian city. Methods: A qualitative research design consisting of semi-structured focus group interviews and individual interviews with 13 participants representing various support agencies. Results: The analysis resulted in four thematic areas: (1) belonging and a gateway to support and services; (2) balancing user autonomy at the Bench and systems support; (3) the role of a centralised location; and (4) the preventive role of support workers at the Bench. Conclusion: While the Bench fosters a sense of community and provides access to vital services, concerns about illicit activities persist due to its visibility. Nevertheless, its central location facilitates outreach efforts and ensures easy access to support services. The study underscores the preventive role of healthcare and social workers in stabilising the environment and mitigating conflicts. Furthermore, it highlights the importance of central locations for public gathering places, emphasising the proximity to low-threshold services. The Bench represents a crucial venue for establishing relationships and providing assistance to individuals who use drugs, contributing to harm reduction efforts in the community.

Detecting drug transfers via the drop-off method: A supervised model approach using AIS data

Maritime security is of tremendous importance in countering drug trafficking, particularly through sea-based routes. In this paper, we address the pressing need for effective detection methods by introducing a novel approach utilizing Automatic Identification System (AIS) data. Our focus lies on detecting the ‘drop-off’ method, a prevalent technique for contraband smuggling at sea. Unlike existing research, primarily employing unsupervised methods, we propose a supervised model specifically tailored to this illicit activity, with a particular emphasis on its application to fishing vessels.Our model significantly reduces the number of data points requiring classification by the observer by 70% , thereby enhancing the efficiency of the drop-off detection process. By employing a Long Short-Term Memory (LSTM) model, our approach demonstrates a change from traditional methods and offers advantages in capturing complex temporal patterns inherent in ‘drop-off’ activities. The rationale behind choosing LSTM lies in its ability to effectively model sequential data, which is essential for detecting drug traffic activities at sea where patterns are subtle and dynamic.Moreover, this model holds the potential for integration into real-time surveillance systems, thereby enhancing operational capabilities in detecting and preventing drug traffic. The generalizability of our model makes for considerable potential in enhancing maritime security efforts and providing assistance in countering drug traffic on a global scale. Importantly, our model outperforms both baseline models, underscoring its effectiveness and superiority in addressing the specific challenges posed by ‘drop-off’ detection. For more information and access to the code repository, please visit this link.

Setting new benchmarks for combating financial crimes and ensuring the safety and security of America's digital financial landscape

The increasing digitalization of financial services in the United States has introduced significant challenges in combating financial crimes, including fraud, money laundering, and cybercrimes. As digital transactions become more pervasive, so too do the opportunities for illicit activities that exploit the vulnerabilities of these systems. This research seeks to establish new benchmarks for combating financial crimes and ensuring the security of America's digital financial landscape. By leveraging advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), and blockchain, this study explores innovative approaches to enhancing fraud detection, improving transparency, and reducing the risk of money laundering. A mixed-methods approach was employed, combining qualitative analysis of existing regulatory frameworks with quantitative case studies to assess the effectiveness of these technologies in real-world applications. The results indicate that AI and ML significantly enhance financial institutions' ability to detect fraudulent activities in real-time, reducing false positives and allowing for more efficient resource allocation. Furthermore, blockchain technology improved financial transactions' traceability, enhancing financial systems' transparency and accountability. The study concludes with recommendations for policy and practice, emphasizing the need for adaptive regulatory frameworks, increased investment in advanced technologies, and more vital collaboration between the public and private sectors. These findings suggest that a proactive approach, integrating technological innovation with strategic policy measures, is crucial for safeguarding America's financial infrastructure against emerging threats in the digital era.

NAVIGATING THE SHADOWS: UNRAVELING THE LEGAL AND ETHICAL CHALLENGES OF THE DARK WEB AND CRYPTOCURRENCY

In the internet, where anonymity reigns supreme and transactions occur beyond the scrutiny of traditional oversight, two phenomena have emerged as both enigmatic and contentious: the Dark Web and cryptocurrency. This clandestine realm, shrouded in mystery and intrigue, presents a host of legal and ethical challenges that intersect with broader debates surrounding privacy, regulation, and criminality.The emergence of the Dark Web alongside the proliferation of cryptocurrency has presented unprecedented challenges to legal frameworks and ethical considerations. This endeavors to dissect the intricate web of legal and ethical quandaries that arise from the interplay between the Dark Web and cryptocurrency. By delving into the clandestine nature of the Dark Web, we explore the various illicit activities facilitated, ranging from drug trafficking to human exploitation, and the ensuing difficulties in law enforcement efforts. Furthermore, we scrutinize the decentralized and pseudonymous nature of cryptocurrency, which amplifies the complexities of tracing financial transactions and holding perpetrators accountable.

CYBER LAW AND CYBER CRIME

Inthe rapidly evolving digital landscape, the proliferation of technology has brought about unprecedented opportunities for innovation and connectivity, but it has also given rise to new challenges and threats. Cyber law, as a field, has emerged to navigate the legal dimensions of this digital realm, addressing issues ranging from data privacy and intellectual property rights to cybercrime and cybersecurity.This abstract explores the intersection of cyber law and cybercrime, delving into the evolving legal frameworks designed to govern cyberspace and combat illicit activities within it. With the internet transcending geographical boundaries, traditional legal frameworks often struggle to keep pace with the complexities of cyber incidents. Consequently, the development of specialized cyber laws becomes imperative to ensure effective regulation and enforcement. Keywords: Data correction, online behavior, cyber bulling, ethical hacking

Financial crime risk assessment: machine learning insights into ownership structures in secrecy firms

ABSTRACT This study examines the relationship between corporate secrecy and financial crime, presenting an analytical framework to strengthen risk assessment efforts. We develop secrecy indicators using corporate ownership data from over 2.6 million firms across eight European countries. These indicators are validated using machine learning models built upon evidence of crime committed by firms and/or their owners. The results demonstrate robust predictive power: firms with complex structures and owners from high-risk jurisdictions show a higher likelihood of engaging in illicit activities. Incorporating macro-level information, such as geographic location and economic sector, enhances the understanding of this phenomenon. These findings advance empirical knowledge about the nexus between secrecy firms and crime, offering anti-money laundering authorities novel machine learning tools for effective risk assessment.

Analysis of Implementation of Anti-Money Laundering Mechanisms in Blockchain-Based Smart Contracts under Indonesian Regulation

One of the most widely adopted forms of information technology application today is electronic trade through the internet network to make transactions. This transaction system was initially centralized, but with the risk of system failure and hacking, distributed systems such as blockchain were developed to improve security and efficiency. Blockchain also gave birth to derivative technologies such as smart contracts, automated blockchain-based digital agreements. Despite offering many advantages, the anonymity and decentralization of smart contracts raise concerns of misuse for illicit activities such as money laundering. Therefore, regulatory updates in Indonesia are needed to regulate the validity of smart contracts and money laundering actions through this technology. This article analyses the validity and regulation of smart contracts and efforts to implement the AML system in blockchain-based transactions in Indonesia.

Understanding the role of street network configurations in the placement of illegitimately operating facilities

AbstractThe role of street networks in shaping the spatial distribution of crime has become a foundational component within environmental criminology. Most studies, however, have focused on opportunistic crime types, such as property offenses. In this study, we instead research a theoretically distinct phenomenon by examining the placement of venues that host criminal activity. In particular, we study the relationship between network structure and the placement of illicit massage businesses, which operate on the intersections of illicit and legitimate activity by hosting illicit commercial sex under the guise of legitimate massage. We model their placement as a function of two network metrics: betweenness, which measures a street's usage potential, and a variant called “local betweenness,” which measures the potential of nearby streets. Multilevel models are used to examine the importance of these street‐level metrics while accounting for tract‐level covariates. Our findings demonstrate that, unlike property crimes, illicit massage businesses tend to be located on streets that are themselves quiet but that are close to areas of high activity. Such locations seem to combine accessibility and discretion, and therefore, represent ideal conditions for such businesses to thrive. Our findings can inform problem‐oriented approaches to prevent the harms associated with illegitimately operating businesses.

Immigrants and innovators: financial inclusion and the ‘de-banking’ of the digital asset industry

ABSTRACT ‘De-banking’ refers to banks limiting or withdrawing banking services from perceived ‘high-risk’ clients. ‘High risk’ is not merely commercial risk relating to a client’s ability to pay. It also includes ‘non-commercial’ risks such as client dealings in illicit funds or activities. Debanking decisions might also be made on operational-cost grounds where continued servicing of a particular customer base becomes uneconomical. In Australia, the de-banking issue has impacted migrant communities. More recently, de-banking has impeded the ability of digital assets service providers. Under the guise of rigorous compliance legal standards, Australian banks have limited or even cut-off banking services to many Australian digital asset firms. As the financial gatekeepers to the Australian economy, Australian banks (already very protected from competitors) are undermining an industry that could potentially compete for market share. Examining strategies adopted in other jurisdictions, Australian policy makers should supplement the introduction of a licensing regime, as proposed by the Digital Assets (Market Regulation) Bill 2023 (Cth), with additional balanced measures in order to support innovation in the digital asset sector and maintain financial integrity.

Cryptocurrency and its Nexus with Money Laundering and Terrorism Financing within the Framework of FATF Recommendations

Cryptocurrency has emerged as a viable alternative to conventional payment systems, offering its users notable advantages such as cost efficiency and rapid transaction processing. However, cryptocurrencies’ decentralized nature, anonymity, and susceptibility to cyber threats introduce the potential for their exploitation in illicit activities, notably money laundering and terrorism financing (ML/TF). This scholarly investigation seeks to investigate the roles played by international organizations actively combating such criminal activities. In this academic context, a meticulous consideration of the inherent risks associated with the aforementioned criminal endeavors is undertaken, aligning with the guidelines and recommendations set forth by the Financial Action Task Force (FATF). The study underscores a significant finding that cryptocurrency accounts can be opened anonymously, with no centralized registry to monitor cryptocurrency ownership. This characteristic poses a formidable challenge in confiscating funds linked to terrorist activities. Even in instances where cryptocurrency transactions may be traced, accessing this critical data necessitates the involvement of third-party entities, given that cryptocurrency transactions are recorded in decentralized ledgers spanning multiple jurisdictions. Furthermore, the study underscores the imperative of coordination and information exchange as indispensable elements in the ongoing battle against organized and transnational criminal activities, specifically ML/TF. Moreover, the study elucidates the incongruence between cryptocurrency and FATF regulations governing electronic fund transfers, as cryptocurrencies can make transactions through opaque and unregulated conduits, such as the deep web.

A Hybrid Framework for Maritime Surveillance: Detecting Illegal Activities through Vessel Behaviors and Expert Rules Fusion.

Maritime traffic is essential for global trade but faces significant challenges, including navigation safety, environmental protection, and the prevention of illicit activities. This work presents a framework for detecting illegal activities carried out by vessels, combining navigation behavior detection models with rules based on expert knowledge. Using synthetic and real datasets based on the Automatic Identification System (AIS), we structured our framework into five levels based on the Joint Directors of Laboratories (JDL) model, efficiently integrating data from multiple sources. Activities are classified into four categories: illegal fishing, suspicious activity, anomalous activity, and normal activity. To address the issue of a lack of labels and integrate data-driven detection with expert knowledge, we employed a stack ensemble model along with active learning. The results showed that the framework was highly effective, achieving 99% accuracy in detecting illegal fishing and 92% in detecting suspicious activities. Furthermore, it drastically reduced the need for manual checks by specialists, transforming experts' tacit knowledge into explicit knowledge through the models and allowing continuous updates of maritime domain rules. This work significantly contributes to maritime surveillance, offering a scalable and efficient solution for detecting illegal activities in the maritime domain.

Construction of TCM Institution Image: A Corpus-Based Comparative Critical Discourse Study of Chinese and Western Mainstream Media Reports

This study conducts a corpus-assisted comparative analysis of the media images of Traditional Chinese Medicine (TCM) institutions in Chinese and Western media contexts. Applying Fairclough’s three-dimensional critical discourse analysis (CDA) model alongside the TCM Social Image Evaluation Index System, this study explores media image construction and ideological factors shaping the perception of TCM institutions globally through an analysis of linguistic features in keywords, evaluative collocates and modal verbs. It’s found that Chinese media present TCM institutions in a positive light, depicting them as multifaceted organizations with a rich cultural legacy and robust capabilities in medical and scientific research. They showcase TCM institutions as cooperative, promising in integration with modern healthcare, efficient, rapid, flexible, innovative, and imbued with humanitarian values especially during the COVID-19 epidemic. Western media, however, adopt a more cautious stance. They tend to frame TCM institutions as providers of alternative medicine and temporary solutions, with a focus on health preservation, leisure and overall well-being. Concerns have also been raised about the potential illicit activities associated with some massage schools. These disparities in presenting TCM institutions reflect cultural, political and audience demand differences between China and the West. The results of this study contribute to a deeper understanding of the media portrayal of TCM institutions, provide insights into the challenges facing the global recognition and acceptance of TCM institutions, and suggest strategies for Chinese media, TCM practitioners, and policymakers to enhance their international image.

Copyright Violation in Works of Art Using the Form of Non-Fungible Tokens (NFT) in the Era of Digitalization

Intellectual Property Rights (IPR) are exclusive rights granted by the state to individuals or groups to safeguard their intellectual works. Such works may include inventions, works of art, writings, designs, and trademarks, among others. In Indonesia, IPR is regulated by several laws, including the Patents Law (Law Number 13 of 2016), Copyright Law (Law Number 28 of 2014), and Marks and Geographical Indications Law (Law Number 20 of 2016), among others. These rights encompass copyrights, patents, trademarks, industrial designs, trade secrets, and other related rights. The purpose of IPR protection is to encourage innovation and creativity by providing legal protection for the fruits of intellectual labor, thereby enabling individuals and organizations to reap the benefits of their efforts. Businesses and individuals must understand IPR laws and regulations, as they affect the ownership, licensing, and transfer of intellectual property assets. Violation of two-dimensional works of art and NFTs is a form of crime in digital economic activities, in this case, the government must intervene to overcome this crime. The issue at hand is a direct result of insufficient legal regulations governing the digital realm. Cybercrime, which encompasses a multitude of illicit or perilous activities that take place in the virtual world or through the utilization of computer and internet technology, encompasses copyright infringement. This form of wrongdoing entails the contravention of intellectual property rights, such as the unlawful distribution of music or films, software piracy, or the unauthorized use of trademarks. Legal protection pertains to regulations that are concerned with digital works of art.

DarkMor: A framework for darknet traffic detection that integrates local and spatial features

The dark web, as an integral part of the multi-layered structure of the internet, provides anonymity and high levels of concealment absent on the surface web. Users can engage in various online activities without leaving any trace, making the dark web a hub for illicit activities, such as drug and weapon trafficking. Consequently, this poses a significant threat to social order and network security. However, due to the high concealment of the dark web, traditional detection methods suffer with insufficient extraction of characteristic information from darknet traffic and inadequate consideration of feature correlations. As a result, the accuracy of these conventional detection methods in detecting the dark web in real network environments is suboptimal. This paper proposes a darknet traffic detection framework called DarkMor to address these challenges. It integrates local and spatial features, automates feature mining and fusion, and models spatial relationship between features to fully exploit their potential. The core components of DarkMor consist of the feature fusion module and the traffic perception module. Using an improved feature tokenizer transformer architecture, the feature fusion module enhances the extraction of local features within high-dimensional feature clusters, effectively combining local feature information with global context. Additionally, the traffic perception module leverages a temporal model that incorporates self-attention mechanisms to learn the spatiotemporal characteristics of fused features, thereby further enhancing the model’s detection. Experimental results demonstrated that DarkMor achieved an accuracy of 97.78% on real network datasets, surpassing the latest cross-modal darknet traffic detection models. Furthermore, DarkMor maintained an accuracy of 97.57% even in network environments with reduced training samples, confirming the feasibility and robustness of the proposed detection framework.

Unveiling Territorialities: Small Drones for Ethnographic Research on Environmental Conflicts

Researching environmental conflicts can be a challenging ethnographic endeavor, especially when their causes are influenced (in)directly by illicit stakeholders and activities. In this article we explore the potential of small drones for better grasping the complexity of environmental conflicts from an activist research and an engaged ethnography standpoint. Through a case study in Michoacán (Mexico), we show how we used small drones for ethnographic research in three different stages: before takeoff, during flights, and after landing. We also show how drones helped us unveil illegal and criminal territorialities previously unnoticed in the study area, which were key to understanding the environmental conflict, its root causes, and its present dynamics. Based on our findings, we present a methodological proposal for using small drones for ethnographic research. We also discuss some safety and ethical considerations related to our proposal. We conclude by suggesting some avenues for future research to further explore the ethnographic potential of small drones in environmental conflicts and other research areas.

Legality of Bitcoin in the Perspective of Fiqh Muamalah

This study investigates the legality of Bitcoin from the perspective of Fiqh Muamalah through qualitative analysis and literature review. Fiqh Muamalah, an Islamic jurisprudential framework, regulates economic transactions according to Sharia law. The research examines whether Bitcoin, as a digital currency, aligns with Islamic legal and ethical standards by analyzing scholarly articles, religious texts, and fatwas issued by Islamic scholars. Key considerations include Bitcoin's nature as a currency, its speculative role, and potential for illicit activities. Findings reveal diverse scholarly opinions, with some viewing Bitcoin as permissible under specific conditions, while others raise concerns about its speculative nature and lack of intrinsic value. This study aims to provide comprehensive insights into Bitcoin's legality within Fiqh Muamalah, offering valuable information for policymakers, financial institutions, and Muslim investors.

Unravelling the dynamics of child sexual exploitation material circulation on the Dark Web.

In recent years, there has been a significant increase in the detection of child sexual exploitation material (CSEM), with perpetrators increasingly turning to advanced encryption technologies to conceal their activities. This study delves into data from a Brazilian Federal Police operation on the Tor network, aimed at disrupting these illicit activities. We uncovered patterns indicating strong user preferences for certain content categories, suggesting the existence of distinct groups with shared interests. Additionally, our findings reveal consistent activity patterns among users, including specific 24-hour, 12-hour, and 6-hour consumption cycles. This research offers insights into the online behavior related to CSEM, providing a foundation for further investigation and the development of effective policy measures.