Authors: Trung-Nghia Le, Huy H Nguyen, Junichi Yamagishi, Isao Echizen
Published: 2022-02-13 06:53:39+00:00
AI Summary
This research paper provides a comprehensive review of deepfake generation and detection methods, highlighting their evolution and challenges. It explores various approaches to improve the robustness of deepfake detection across diverse media types, suggesting future research directions.
Abstract
Recent advances in deep learning have led to substantial improvements in deepfake generation, resulting in fake media with a more realistic appearance. Although deepfake media have potential application in a wide range of areas and are drawing much attention from both the academic and industrial communities, it also leads to serious social and criminal concerns. This chapter explores the evolution of and challenges in deepfake generation and detection. It also discusses possible ways to improve the robustness of deepfake detection for a wide variety of media (e.g., in-the-wild images and videos). Finally, it suggests a focus for future fake media research.
Key findings
The paper highlights the limitations of current deepfake detection methods, particularly their vulnerability to adversarial attacks and poor generalization to unseen scenarios. It emphasizes the need for more robust methods that can handle diverse media types and incorporate fact verification for improved reliability. Data augmentation is suggested as a key strategy for improving robustness.
Approach
The paper reviews existing deepfake generation and detection methods, categorizing them by approach (e.g., autoencoder, GAN-based) and application (e.g., identity swapping, expression reenactment). It analyzes limitations of existing methods, particularly robustness to unseen scenarios and adversarial attacks, and proposes improvements like data augmentation and fact verification.
Datasets
DF-TIMIT, UADFV, FaceForensics++, Google DFD, Facebook DFDC, Celeb-DF, DeeperForensics, WildDeepfake, FFIW, OpenForensics
Model(s)
Various models are mentioned but not explicitly used as the main contribution, including autoencoders, GANs, MesoNet, XceptionNet, EfficientNet, CapsuleNet, and others used in cited works. The paper focuses on analyzing existing models rather than proposing a novel one.
Author countries
Japan