D$^3$: Scaling Up Deepfake Detection by Learning from Discrepancy

The boom of Generative AI brings opportunities entangled with risks and concerns. In this work, we seek a step toward a universal deepfake detection system with better generalization and robustness, to accommodate the responsible deployment of diverse image generative models. We do so by first scaling up the existing detection task setup from the one-generator to multiple-generators in training, during which we disclose two challenges presented in prior methodological designs. Specifically, we reveal that the current methods tailored for training on one specific generator either struggle to learn comprehensive artifacts from multiple generators or tend to sacrifice their ability to identify fake images from seen generators (i.e., In-Domain performance) to exchange the generalization for unseen generators (i.e., Out-Of-Domain performance). To tackle the above challenges, we propose our Discrepancy Deepfake Detector (D$^3$) framework, whose core idea is to learn the universal artifacts from multiple generators by introducing a parallel network branch that takes a distorted image as extra discrepancy signal to supplement its original counterpart. Extensive scaled-up experiments on the merged UFD and GenImage datasets with six detection models demonstrate the effectiveness of our framework, achieving a 5.3% accuracy improvement in the OOD testing compared to the current SOTA methods while maintaining the ID performance.