Deepfake Synthesis vs. Detection: An Uneven Contest

TL;DR

This paper provides a comprehensive empirical analysis showing that current deepfake detection methods, including human judgment, struggle against modern, sophisticated deepfake synthesis techniques, highlighting a significant gap in detection capabilities.

Contribution

It offers an extensive evaluation of state-of-the-art detection methods against recent deepfake generation techniques, revealing their limitations and emphasizing the need for improved detection strategies.

Findings

Detection models perform poorly on modern deepfakes

Humans also struggle to identify high-quality deepfakes

There is a critical gap between deepfake generation and detection capabilities

Abstract

The rapid advancement of deepfake technology has significantly elevated the realism and accessibility of synthetic media. Emerging techniques, such as diffusion-based models and Neural Radiance Fields (NeRF), alongside enhancements in traditional Generative Adversarial Networks (GANs), have contributed to the sophisticated generation of deepfake videos. Concurrently, deepfake detection methods have seen notable progress, driven by innovations in Transformer architectures, contrastive learning, and other machine learning approaches. In this study, we conduct a comprehensive empirical analysis of state-of-the-art deepfake detection techniques, including human evaluation experiments against cutting-edge synthesis methods. Our findings highlight a concerning trend: many state-of-the-art detection models exhibit markedly poor performance when challenged with deepfakes produced by modern synthesis techniques, including poor performance by human participants against the best quality deepfakes. Through extensive experimentation, we provide evidence that underscores the urgent need for continued refinement of detection models to keep pace with the evolving capabilities of deepfake generation technologies. This research emphasizes the critical gap between current detection methodologies and the sophistication of new generation techniques, calling for intensified efforts in this crucial area of study.