Multi-to-Binary: A Generalizable Deepfake Detection Approach with Multi-Classification Guidance

Abstract

Visual content forgery techniques, such as Deepfake, have rapidly advanced in recent years. Due to the potential misuse of these techniques for malicious purposes, there is increasing attention to the corresponding detection methods. Most existing methods focus on specific forgery patterns, making it difficult to detect forgeries with unknown or evolving patterns. In this work, we propose a novel forgery detection framework designed to extract comprehensive features utilizing multiple classification models. More specifically, our proposed framework consists of both binary-classification and multi-classification models working collaboratively, enhanced by innovative fusion and freezing mechanisms to improve accuracy and efficiency. We conducted extensive experiments to evaluate the performance of our approach. The results demonstrate that our approach outperforms state-of-the-art techniques in terms of generalization to new forgery patterns and robustness against various types of forgeries. This makes our method highly effective for real-world applications where forgeries can be diverse and sophisticated.

Keywords: Deepfake Detection, Multi-classification guidance, Freezing mechanism, Label loss