Deepfakes in Criminal Investigations: Interdisciplinary Research Directions for CMC Research

• URL: http://arxiv.org/abs/2507.03457v1
• Date: Fri, 04 Jul 2025 10:22:50 GMT
• Title: Deepfakes in Criminal Investigations: Interdisciplinary Research Directions for CMC Research
• Abstract summary: Deepfakes may enable novel applications in high-stakes contexts such as criminal investigations.<n>We adopt an interdisciplinary approach, drawing on computer science, philosophy, and law, to examine what it takes to responsibly use deepfakes in criminal investigations.<n>Our analysis outlines key research directions for the CMC community and underscores the need for interdisciplinary collaboration in this evolving domain.
• Score: 1.294895529233679
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The emergence of deepfake technologies offers both opportunities and significant challenges. While commonly associated with deception, misinformation, and fraud, deepfakes may also enable novel applications in high-stakes contexts such as criminal investigations. However, these applications raise complex technological, ethical, and legal questions. We adopt an interdisciplinary approach, drawing on computer science, philosophy, and law, to examine what it takes to responsibly use deepfakes in criminal investigations and argue that computer-mediated communication (CMC) research, especially based on social media corpora, can provide crucial insights for understanding the potential harms and benefits of deepfakes. Our analysis outlines key research directions for the CMC community and underscores the need for interdisciplinary collaboration in this evolving domain.

Related papers

• Cybercrime and Computer Forensics in Epoch of Artificial Intelligence in India [0.0]
  This study scrutinizes the AI "dual-use" dilemma, functioning as both a cyber-threat vector and forensic automation mechanism.<n>While Machine Learning offers high accuracy in pattern recognition, it introduces vulnerabilities regarding data poisoning and algorithmic bias.<n>Findings highlight a critical tension between the Act's data minimization principles and forensic data retention requirements.
  arXiv  Detail & Related papers  (2025-12-16T19:39:22Z)
• A Neuro-Symbolic Multi-Agent Approach to Legal-Cybersecurity Knowledge Integration [53.58687192914018]
  This work provides a first step towards intelligent systems capable of navigating the increasingly intricate cyber-legal domain.<n>We demonstrate promising initial results on multilingual tasks.
  arXiv  Detail & Related papers  (2025-10-27T15:46:02Z)
• SoK: Advances and Open Problems in Web Tracking [71.54586748169943]
  Web tracking is a pervasive and opaque practice that enables personalized advertising, and conversion tracking.<n>Web tracking is undergoing a once-in-a-generation transformation driven by shifts in the advertising industry, the adoption of anti-tracking countermeasures by browsers, and the growing enforcement of emerging privacy regulations.<n>This Systematization of Knowledge (SoK) aims to consolidate and synthesize this wide-ranging research, offering a comprehensive overview of the technical mechanisms, countermeasures, and regulations that shape the modern and rapidly evolving web tracking landscape.
  arXiv  Detail & Related papers  (2025-06-16T23:30:54Z)
• When Anti-Fraud Laws Become a Barrier to Computer Science Research [2.819725769698229]
  We analyze and taxonomize the anti-fraud and deception issues that arise in several areas of computer science research.<n>We highlight the importance of anti-fraud laws in two research fields of great policy importance: attacking or auditing AI systems, and research involving legal identification.
  arXiv  Detail & Related papers  (2025-02-04T23:14:09Z)
• Open Problems in Mechanistic Interpretability [61.44773053835185]
  Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities.<n>Despite recent progress toward these goals, there are many open problems in the field that require solutions.
  arXiv  Detail & Related papers  (2025-01-27T20:57:18Z)
• Understanding Audiovisual Deepfake Detection: Techniques, Challenges, Human Factors and Perceptual Insights [49.81915942821647]
  Deep Learning has been successfully applied in diverse fields, and its impact on deepfake detection is no exception. Deepfakes are fake yet realistic synthetic content that can be used deceitfully for political impersonation, phishing, slandering, or spreading misinformation. This paper aims to improve the effectiveness of deepfake detection strategies and guide future research in cybersecurity and media integrity.
  arXiv  Detail & Related papers  (2024-11-12T09:02:11Z)
• Advancing Crime Linkage Analysis with Machine Learning: A Comprehensive Review and Framework for Data-Driven Approaches [0.0]
  Crime linkage is the process of analyzing criminal behavior data to determine whether a pair or group of crime cases are connected or belong to a series of offenses. This study aims to understand the challenges faced by machine learning approaches in crime linkage and to support foundational knowledge for future data-driven methods.
  arXiv  Detail & Related papers  (2024-10-30T18:22:45Z)
• A Smart City Infrastructure Ontology for Threats, Cybercrime, and Digital Forensic Investigation [2.2530496464901106]
  Efforts have been made to assist digital forensic investigators (DFI) and law enforcement agencies (LEA) in their investigative efforts.<n>Forensic tool innovations and developments, such as the Unified Cyber Ontology (UCO) and Cyber-investigation Standard Expression (CASE), have been proposed to assist DFI and LEA.<n>To mitigate weaknesses in both and to ensure a safer cyber-physical environment for all, we propose the Smart City Ontological Expression (SCOPE)
  arXiv  Detail & Related papers  (2024-08-04T13:20:01Z)
• Deepfake Media Forensics: State of the Art and Challenges Ahead [51.33414186878676]
  AI-generated synthetic media, also called Deepfakes, have influenced so many domains, from entertainment to cybersecurity. Deepfake detection has become a vital area of research, focusing on identifying subtle inconsistencies and artifacts with machine learning techniques. This paper reviews the primary algorithms that address these challenges, examining their advantages, limitations, and future prospects.
  arXiv  Detail & Related papers  (2024-08-01T08:57:47Z)
• Report of the 1st Workshop on Generative AI and Law [78.62063815165968]
  This report presents the takeaways of the inaugural Workshop on Generative AI and Law (GenLaw) A cross-disciplinary group of practitioners and scholars from computer science and law convened to discuss the technical, doctrinal, and policy challenges presented by law for Generative AI.
  arXiv  Detail & Related papers  (2023-11-11T04:13:37Z)
• SoK: Cross-border Criminal Investigations and Digital Evidence [5.864907136562177]
  The aim of this article is to analyse the current state of practice of cross-border investigations. We recall all the challenges raised in the literature and discuss them from a more practical yet global perspective.
  arXiv  Detail & Related papers  (2022-05-25T17:03:38Z)
• An Uncommon Task: Participatory Design in Legal AI [64.54460979588075]
  We examine a notable yet understudied AI design process in the legal domain that took place over a decade ago. We show how an interactive simulation methodology allowed computer scientists and lawyers to become co-designers.
  arXiv  Detail & Related papers  (2022-03-08T15:46:52Z)
• A Search Engine for Scientific Publications: a Cybersecurity Case Study [0.7734726150561086]
  This work proposes a new search engine for scientific publications which combines both information retrieval and reading comprehension algorithms. The proposed solution although being applied to the context of cybersecurity exhibited great generalization capabilities and can be easily adapted to perform under other distinct knowledge domains.
  arXiv  Detail & Related papers  (2021-06-30T20:10:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.