Abstract
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 at this https URL.
Abstract (translated)
深度伪造（Deepfakes）技术利用先进的AI方法，如变分自编码器和生成对抗网络，已从研究和娱乐应用发展成为恶意活动的工具，对数字信任构成了重大威胁。当前的深度伪造检测技术已经从基于CNN的方法，这些方法侧重于局部异常特征，演进为使用视觉变压器和CLIP等多模态模型的更先进方法，这些方法能够捕捉全局异常并提高跨域泛化能力。尽管最近取得了进展，但最先进的深度伪造探测器仍然面临处理新兴生成模型带来的分布变化以及在深度伪造数据集中真伪样本之间严重类不平衡的问题的重大挑战，这限制了它们的鲁棒性和检测准确性。 为了解决这些问题，我们提出了一种结合动态损失再加权和基于排名优化框架的方法，在不均衡的数据集条件下实现了卓越的泛化能力和性能。该代码可在[此处](https://example.com)获取。
URL
https://arxiv.org/abs/2505.02182
