Deepfake Technology Unveiled: The Commoditization of AI and Its Impact on Digital Trust

• URL: http://arxiv.org/abs/2506.07363v2
• Date: Tue, 15 Jul 2025 14:07:07 GMT
• Title: Deepfake Technology Unveiled: The Commoditization of AI and Its Impact on Digital Trust
• Authors: Claudiu Popa, Rex Pallath, Liam Cunningham, Hewad Tahiri, Abiram Kesavarajah, Tao Wu,
• Abstract summary: Deepfake technology enables fraud, misinformation, and the erosion of authenticity in multimedia.<n>Using cost-effective, easy to use tools such as Runway, Rope, and ElevenLabs, we explore how realistic deepfakes can be created with limited resources.<n>We emphasize the urgent need for regulatory frameworks, public awareness, and collaborative efforts to maintain trust in digital media.
• Score: 1.1402735220778926
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deepfake Technology Unveiled: The Commoditization of AI and Its Impact on Digital Trust. With the increasing accessibility of generative AI, tools for voice cloning, face-swapping, and synthetic media creation have advanced significantly, lowering both financial and technical barriers for their use. While these technologies present innovative opportunities, their rapid growth raises concerns about trust, privacy, and security. This white paper explores the implications of deepfake technology, analyzing its role in enabling fraud, misinformation, and the erosion of authenticity in multimedia. Using cost-effective, easy to use tools such as Runway, Rope, and ElevenLabs, we explore how realistic deepfakes can be created with limited resources, demonstrating the risks posed to individuals and organizations alike. By analyzing the technical and ethical challenges of deepfake mitigation and detection, we emphasize the urgent need for regulatory frameworks, public awareness, and collaborative efforts to maintain trust in digital media.

Related papers

• Bridging Ethical Principles and Algorithmic Methods: An Alternative Approach for Assessing Trustworthiness in AI Systems [0.0]
  This paper introduces an assessment method that combines the ethical components of Trustworthy AI with the algorithmic processes of PageRank and TrustRank.<n>The goal is to establish an assessment framework that minimizes the subjectivity inherent in the self-assessment techniques prevalent in the field.
  arXiv  Detail & Related papers  (2025-06-28T06:27:30Z)
• AI-Powered Spearphishing Cyber Attacks: Fact or Fiction? [0.0]
  Deepfake technology is capable of replacing the likeness or voice of one individual with another with alarming accuracy.<n>This paper investigates the threat posed by malicious use of this technology, particularly in the form of spearphishing attacks.<n>It uses deepfake technology to create spearphishing-like attack scenarios and validate them against average individuals.
  arXiv  Detail & Related papers  (2025-02-03T00:02:01Z)
• Misrepresented Technological Solutions in Imagined Futures: The Origins and Dangers of AI Hype in the Research Community [0.060998359915727114]
  We look at the origins and risks of AI hype to the research community and society more broadly. We propose a set of measures that researchers, regulators, and the public can take to mitigate these risks and reduce the prevalence of unfounded claims about the technology.
  arXiv  Detail & Related papers  (2024-08-08T20:47:17Z)
• 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)
• Deepfakes, Misinformation, and Disinformation in the Era of Frontier AI, Generative AI, and Large AI Models [7.835719708227145]
  Deepfakes and the spread of m/disinformation have emerged as formidable threats to the integrity of information ecosystems worldwide. We highlight the mechanisms through which generative AI based on large models (LM-based GenAI) craft seemingly convincing yet fabricated contents. We introduce an integrated framework that combines advanced detection algorithms, cross-platform collaboration, and policy-driven initiatives.
  arXiv  Detail & Related papers  (2023-11-29T06:47:58Z)
• Common (good) practices measuring trust in HRI [55.2480439325792]
  Trust in robots is widely believed to be imperative for the adoption of robots into people's daily lives. Researchers have been exploring how people trust robot in different ways. Most roboticists agree that insufficient levels of trust lead to a risk of disengagement.
  arXiv  Detail & Related papers  (2023-11-20T20:52:10Z)
• The Age of Synthetic Realities: Challenges and Opportunities [85.058932103181]
  We highlight the crucial need for the development of forensic techniques capable of identifying harmful synthetic creations and distinguishing them from reality. Our focus extends to various forms of media, such as images, videos, audio, and text, as we examine how synthetic realities are crafted and explore approaches to detecting these malicious creations. This study is of paramount importance due to the rapid progress of AI generative techniques and their impact on the fundamental principles of Forensic Science.
  arXiv  Detail & Related papers  (2023-06-09T15:55:10Z)
• Attention Paper: How Generative AI Reshapes Digital Shadow Industry? [41.38949535910943]
  Black and shadow internet industries pose potential risks that can be identified and managed through digital risk management (DRM) The paper will explore the new black and shadow techniques triggered by generative AI technology and provide insights for building the next-generation DRM system.
  arXiv  Detail & Related papers  (2023-05-26T08:03:50Z)
• AI Maintenance: A Robustness Perspective [91.28724422822003]
  We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance. We propose an AI model inspection framework to detect and mitigate robustness risks. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
  arXiv  Detail & Related papers  (2023-01-08T15:02:38Z)
• Proceedings of the Artificial Intelligence for Cyber Security (AICS) Workshop at AAAI 2022 [55.573187938617636]
  The workshop will focus on the application of AI to problems in cyber security. Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities.
  arXiv  Detail & Related papers  (2022-02-28T18:27:41Z)
• Trustworthy AI: A Computational Perspective [54.80482955088197]
  We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
  arXiv  Detail & Related papers  (2021-07-12T14:21:46Z)

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.