Deepfake Media Forensics: State of the Art and Challenges Ahead

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Authors: Irene Amerini, Mauro Barni, Sebastiano Battiato, Paolo Bestagini, Giulia Boato, Tania Sari Bonaventura, Vittoria Bruni, Roberto Caldelli, Francesco De Natale, Rocco De Nicola, Luca Guarnera, Sara Mandelli, Gian Luca Marcialis, Marco Micheletto, Andrea Montibeller, Giulia Orru', Alessandro Ortis, Pericle Perazzo, Giovanni Puglisi, Davide Salvi, Stefano Tubaro, Claudia Melis Tonti, Massimo Villari, Domenico Vitulano

Published: 2024-08-01 08:57:47+00:00

AI Summary

This research paper reviews the state-of-the-art techniques and challenges in Deepfake media forensics. It covers Deepfake detection, attribution, passive and active authentication methods, and detection in realistic scenarios, examining their advantages and limitations.

Abstract

AI-generated synthetic media, also called Deepfakes, have significantly influenced so many domains, from entertainment to cybersecurity. Generative Adversarial Networks (GANs) and Diffusion Models (DMs) are the main frameworks used to create Deepfakes, producing highly realistic yet fabricated content. While these technologies open up new creative possibilities, they also bring substantial ethical and security risks due to their potential misuse. The rise of such advanced media has led to the development of a cognitive bias known as Impostor Bias, where individuals doubt the authenticity of multimedia due to the awareness of AI's capabilities. As a result, Deepfake detection has become a vital area of research, focusing on identifying subtle inconsistencies and artifacts with machine learning techniques, especially Convolutional Neural Networks (CNNs). Research in forensic Deepfake technology encompasses five main areas: detection, attribution and recognition, passive authentication, detection in realistic scenarios, and active authentication. This paper reviews the primary algorithms that address these challenges, examining their advantages, limitations, and future prospects.

Key findings

The review highlights the challenges in generalizing Deepfake detection across various forgery techniques and the need for robust methods in real-world scenarios (e.g., compressed data, social media). It emphasizes the potential of multimodal analysis and active authentication for improved Deepfake detection and attribution.

Approach

The paper provides a comprehensive review of existing Deepfake detection methods, encompassing various approaches like analyzing inconsistencies and artifacts using Convolutional Neural Networks (CNNs), exploring both texture and artifact analysis, and leveraging multimodal (audio-visual) data for improved accuracy. It also discusses active authentication methods like watermarking and cryptographic signatures.

Datasets

The paper mentions several datasets used in related works, including FaceForensics++, but doesn't specify a single primary dataset used by the authors for their own contribution, as it is a review paper.

Model(s)

Convolutional Neural Networks (CNNs), ResNet-18, StyleGAN2-ADA, various other deep learning models are mentioned as being used in the reviewed works.

Author countries

Italy

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