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

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Authors: Arash Dehghani, Hossein Saberi

Published: 2025-01-07 16:44:45+00:00

AI Summary

This paper reviews state-of-the-art deepfake generation and detection methods, focusing on deep learning technologies. It explores various deepfake types (face swapping, voice conversion, etc.) and analyzes the challenges in identifying manipulated content, highlighting the urgent need for robust detection strategies.

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.

Key findings

The review highlights the ongoing arms race between deepfake generation and detection. It emphasizes the need for robust detection strategies due to the increasing realism and accessibility of deepfake creation tools. Future research directions include the development of advanced detection algorithms and public awareness campaigns.

Approach

The paper provides a comprehensive review of deepfake generation and detection techniques, analyzing various deep learning models such as GANs, VAEs, and diffusion models used in both generation and detection processes. It categorizes deepfakes into image, video, and audio types, examining different approaches within each category.

Datasets

CelebA-HQ, FFHQ, VoxCeleb (mentioned as examples of publicly accessible datasets used for training deepfake models)

Model(s)

Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion models, Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), MesoNet, Xception (mentioned in the context of detection)

Author countries

Iran

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