Deepfakes, created using advanced AI techniques such as Variational Autoencoder and Generative Adversarial Networks, have evolved from research and entertainment applications into tools for malicious activities, posing significant threats to digital trust. Current deepfake detection techniques have evolved from CNN-based methods focused on local artifacts to more advanced approaches using vision transformers and multimodal models like CLIP, which capture global anomalies and improve cross-domain generalization. Despite recent progress, state-of-the-art deepfake detectors still face major challenges in handling distribution shifts from emerging generative models and addressing severe class imbalance between authentic and fake samples in deepfake datasets, which limits their robustness and detection accuracy. To address these challenges, we propose a framework that combines dynamic loss reweighting and ranking-based optimization, which achieves superior generalization and performance under imbalanced dataset conditions. The code is available atthis https URL.
View on arXiv@article{krubha2025_2505.02182,
title={ Robust AI-Generated Face Detection with Imbalanced Data },
author={ Yamini Sri Krubha and Aryana Hou and Braden Vester and Web Walker and Xin Wang and Li Lin and Shu Hu },
journal={arXiv preprint arXiv:2505.02182},
year={ 2025 }
}