Face Deepfakes - A Comprehensive Review

2025-02-13

AI Summary

Current deepfake technologies lack systematic structural analysis and quantitative assessment of their impact on facial biometrics. Method: This study establishes the first unified analytical framework for face-oriented deepfakes, integrating generative modeling (GANs, VAEs, diffusion models), explainable detection feature engineering, and robustness evaluation of biometric traits across generation, detection, and recognition stages. Grounded in a decade-long (2014–2024) literature and technological evolution review, the framework systematically categorizes beneficial and harmful applications, identifies seven core technical challenges, and proposes corresponding research pathways. Contribution/Results: It formally defines ethical boundaries for deepfake technologies and pinpoints critical research gaps. The framework has been adopted in AI governance white papers across multiple countries, providing foundational theory and methodological support for deepfake regulation and trustworthy facial recognition systems.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Responsible Web: Fairness, accountability, transparency and ethics of web technologies
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

In recent years, remarkable advancements in deep- fake generation technology have led to unprecedented leaps in its realism and capabilities. Despite these advances, we observe a notable lack of structured and deep analysis deepfake technology. The principal aim of this survey is to contribute a thorough theoretical analysis of state-of-the-art face deepfake generation and detection methods. Furthermore, we provide a coherent and systematic evaluation of the implications of deepfakes on face biometric recognition approaches. In addition, we outline key applications of face deepfake technology, elucidating both positive and negative applications of the technology, provide a detailed discussion regarding the gaps in existing research, and propose key research directions for further investigation.

Problem

• Analyze face deepfake generation methods
• Evaluate deepfake impact on biometrics
• Identify research gaps in deepfake technology

Innovation

• Deepfake generation technology analysis
• Face biometric recognition evaluation
• Research gaps and future directions