Deepfake Detection via Knowledge Injection
This work addresses the critical need for robust deepfake detection to combat malicious uses of generative AI, representing an incremental improvement through a novel multi-task learning framework.
The paper tackles the problem of deepfake detection by addressing the limited generalization ability of existing methods that overlook real data knowledge, proposing a Knowledge Injection based deepfake Detection (KID) approach that achieves state-of-the-art generalization performance and enhances training convergence speed.
Deepfake detection technologies become vital because current generative AI models can generate realistic deepfakes, which may be utilized in malicious purposes. Existing deepfake detection methods either rely on developing classification methods to better fit the distributions of the training data, or exploiting forgery synthesis mechanisms to learn a more comprehensive forgery distribution. Unfortunately, these methods tend to overlook the essential role of real data knowledge, which limits their generalization ability in processing the unseen real and fake data. To tackle these challenges, in this paper, we propose a simple and novel approach, named Knowledge Injection based deepfake Detection (KID), by constructing a multi-task learning based knowledge injection framework, which can be easily plugged into existing ViT-based backbone models, including foundation models. Specifically, a knowledge injection module is proposed to learn and inject necessary knowledge into the backbone model, to achieve a more accurate modeling of the distributions of real and fake data. A coarse-grained forgery localization branch is constructed to learn the forgery locations in a multi-task learning manner, to enrich the learned forgery knowledge for the knowledge injection module. Two layer-wise suppression and contrast losses are proposed to emphasize the knowledge of real data in the knowledge injection module, to further balance the portions of the real and fake knowledge. Extensive experiments have demonstrated that our KID possesses excellent compatibility with different scales of Vit-based backbone models, and achieves state-of-the-art generalization performance while enhancing the training convergence speed.