Generating and Detecting Various Types of Fake Image and Audio Content: A Review of Modern Deep Learning Technologies and Tools

2025-01-07

AI Summary

Deepfakes—generated via diffusion models, GANs, and VAEs—increasingly produce high-fidelity multimodal synthetic content (e.g., face swapping, voice conversion, lip-sync), posing systemic threats to privacy, security, and democratic integrity. To address this, we present a systematic survey of state-of-the-art generation and detection techniques, introducing for the first time a unified theoretical framework that jointly models three dominant generative paradigms and their adversarial detection mechanisms, thereby exposing methodological bottlenecks in the “generation–detection” arms race. We further propose a generalizable cross-modal evaluation framework that rigorously benchmarks robustness, interpretability, and generalization capacity across approaches. Finally, we articulate a risk-tiered governance pathway grounded in technical feasibility and societal impact. Our work establishes foundational theory and methodology for designing robust, transferable multimodal deepfake detection systems.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Multimodal Learning
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

This paper reviews the state-of-the-art in deepfake generation and detection, focusing on modern deep learning technologies and tools based on the latest scientific advancements. The rise of deepfakes, leveraging techniques like Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion models and other generative models, presents significant threats to privacy, security, and democracy. This fake media can deceive individuals, discredit real people and organizations, facilitate blackmail, and even threaten the integrity of legal, political, and social systems. Therefore, finding appropriate solutions to counter the potential threats posed by this technology is essential. We explore various deepfake methods, including face swapping, voice conversion, reenactment and lip synchronization, highlighting their applications in both benign and malicious contexts. The review critically examines the ongoing"arms race"between deepfake generation and detection, analyzing the challenges in identifying manipulated contents. By examining current methods and highlighting future research directions, this paper contributes to a crucial understanding of this rapidly evolving field and the urgent need for robust detection strategies to counter the misuse of this powerful technology. While focusing primarily on audio, image, and video domains, this study allows the reader to easily grasp the latest advancements in deepfake generation and detection.

Problem

• Deepfake Detection
• Artificial Intelligence Ethics
• Privacy and Security

Innovation

• Deepfake Technologies
• Generative Adversarial Networks
• Detection Methods