Robust Deepfake On Unrestricted Media: Generation And Detection

TL;DR

This paper reviews recent progress in deepfake generation and detection, emphasizing the need for robust methods capable of handling diverse, real-world media to address social and criminal concerns.

Contribution

It provides an overview of the evolution, challenges, and potential improvements in deepfake detection techniques for unrestricted media.

Findings

Deepfake generation has become more realistic with deep learning advances.

Current detection methods face challenges with in-the-wild media.

Future research should focus on robustness and generalization of detection techniques.

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.