Theoretically Principled Balanced Deepfake Detection

Driven by advances in deep neural networks (DNNs), modern generative AI has enabled highly realistic media synthesis while also giving rise to deepfakes that threaten social trust, making reliable deepfake detection essential. Although many DNN-based deepfake detectors have been developed, they overemphasize overall performance but largely ignore class imbalance, which can severely degrade performance and induce asymmetric accuracy between classes as the imbalance increases. Existing imbalance-aware deepfake detection methods mainly address class imbalance but overlook domain-level imbalance and sample hardness, leading to detectors fail to reliably detect fakes from specific domains in real-world deployments. In this work, we study a new bi-level imbalanced learning problem and propose the first balanced deepfake detection approach that jointly addresses class-level and domain-level imbalance by designing a bi-level balanced loss with a general guarantee of $\mathcal{H}$-consistency and a Bayes consistency result established for a special case. Extensive experiments demonstrate that our method consistently outperforms existing losses and can be used in various DNN-based deepfake detectors to improve balanced performance and further improve overall performance.