Deepfake technology presents a wide array of beneficial applications in fields such as art, education, entertainment, and healthcare; however, it also poses significant and growing risks as a potential cybercrime tool. Through the use of Deepfake content, various crimes-including blackmail, defamation, fraud, identity theft, and large-scale disinformation campaigns- can be facilitated. As the technology continues to evolve and grow more sophisticated each day, crimes involving Deepfakes are emerging across a broader range of domains, and anyone can become a target of such attacks. Although numerous studies in the literature address Deepfakes and associated threats from various perspectives, a unified framework that captures the complex and multi-dimensional nature of Deepfake attacks as a cybercrime has yet to be established.This study aims to propose a comprehensive framework for analysing Deepfake attacks across multiple dimensions, thereby supporting strategic and regulatory development, assisting law enforcement and judicial efforts, and contributing to the formation of a common perspective in academic literature.Purpose—The paper aims to propose a comprehensive, multi-dimensional framework for analysing Deepfake attacks as cyber-enabled crimes.Design/methodology/approach—The paper opted for a comprehensive and multi-dimensional framework for analysing Deepfake attacks as cyber-enabled attacks. While existing studies have addressed Deepfakes from different dimensions—like technical, social, or legal- no unified structure has been developed to capture the multifaceted nature. This study seeks to fill that gap and support theoretical, strategic and regulatory development.Findings—The paper provides a ten-dimensional model for analysing Deepfake attacks, covering content type, intent, target, attacker, timing, duration, distribution channel, quality, interaction type and type of harm. Case-based illustrations (e.g., Zelensky video during the Ukrainian Russian war) demonstrate how the proposed framework provides deeper explanatory power compared to existing approaches.Research limitations/implications—Since the model is conceptual and has not yet been empirically tested across large datasets, future research may validate it with cross-national comparison, forensic data, or surveys, and integrate predictive analytics to anticipate emerging Deepfake threats.Practical implications—The framework offers a practical utility for law enforcement, regulators, and forensic experts by enabling a systematic classification of Deepfake attacks and informing targeted countermeasures.Originality/value- This paper is among the first to conceptualise Deepfake attacks as a complex, cyber-enabled through a criminology-informed, multi-dimensional framework. It adds value for academics, law enforcement, and policymakers by offering both theoretical insight and applied relevance.

Exploring a deepfake attack: a multi-dimensional approach

期刊论文
发表日期：

2026年1月6日

查看全文

Zeynep Hazal GÜNAY,
Mustafa ALKAN

摘要

Deepfake technology presents a wide array of beneficial applications in fields such as art, education, entertainment, and healthcare; however, it also poses significant and growing risks as a potential cybercrime tool. Through the use of Deepfake content, various crimes-including blackmail, defamation, fraud, identity theft, and large-scale disinformation campaigns- can be facilitated. As the technology continues to evolve and grow more sophisticated each day, crimes involving Deepfakes are emerging across a broader range of domains, and anyone can become a target of such attacks. Although numerous studies in the literature address Deepfakes and associated threats from various perspectives, a unified framework that captures the complex and multi-dimensional nature of Deepfake attacks as a cybercrime has yet to be established.

This study aims to propose a comprehensive framework for analysing Deepfake attacks across multiple dimensions, thereby supporting strategic and regulatory development, assisting law enforcement and judicial efforts, and contributing to the formation of a common perspective in academic literature.

Purpose—The paper aims to propose a comprehensive, multi-dimensional framework for analysing Deepfake attacks as cyber-enabled crimes.

Design/methodology/approach—The paper opted for a comprehensive and multi-dimensional framework for analysing Deepfake attacks as cyber-enabled attacks. While existing studies have addressed Deepfakes from different dimensions—like technical, social, or legal- no unified structure has been developed to capture the multifaceted nature. This study seeks to fill that gap and support theoretical, strategic and regulatory development.

Findings—The paper provides a ten-dimensional model for analysing Deepfake attacks, covering content type, intent, target, attacker, timing, duration, distribution channel, quality, interaction type and type of harm. Case-based illustrations (e.g., Zelensky video during the Ukrainian Russian war) demonstrate how the proposed framework provides deeper explanatory power compared to existing approaches.

Research limitations/implications—Since the model is conceptual and has not yet been empirically tested across large datasets, future research may validate it with cross-national comparison, forensic data, or surveys, and integrate predictive analytics to anticipate emerging Deepfake threats.

Practical implications—The framework offers a practical utility for law enforcement, regulators, and forensic experts by enabling a systematic classification of Deepfake attacks and informing targeted countermeasures.

Originality/value- This paper is among the first to conceptualise Deepfake attacks as a complex, cyber-enabled through a criminology-informed, multi-dimensional framework. It adds value for academics, law enforcement, and policymakers by offering both theoretical insight and applied relevance.