Abstract
This report presents our approach for the IEEE SP Cup 2025: Deepfake Face Detection in the Wild (DFWild-Cup), focusing on detecting deepfakes across diverse datasets. Our methodology employs advanced backbone models, including MaxViT, CoAtNet, and EVA-02, fine-tuned using supervised contrastive loss to enhance feature separation. These models were specifically chosen for their complementary strengths. Integration of convolution layers and strided attention in MaxViT is well-suited for detecting local features. In contrast, hybrid use of convolution and attention mechanisms in CoAtNet effectively captures multi-scale features. Robust pretraining with masked image modeling of EVA-02 excels at capturing global features. After training, we freeze the parameters of these models and train the classification heads. Finally, a majority voting ensemble is employed to combine the predictions from these models, improving robustness and generalization to unseen scenarios. The proposed system addresses the challenges of detecting deepfakes in real-world conditions and achieves a commendable accuracy of 95.83% on the validation dataset.
Abstract (translated)
该报告介绍了我们对IEEE SP Cup 2025：野外深度伪造人脸检测（DFWild-Cup）大赛的方法，重点在于跨多种数据集进行深度伪造的检测。我们的方法采用先进的骨干模型，包括MaxViT、CoAtNet和EVA-02，并通过监督对比损失进行微调以增强特征分离能力。这些模型因其互补优势而被特别选中。 在MaxViT中，卷积层与步进注意力机制的结合非常适合检测局部特征。相比之下，在CoAtNet中混合使用卷积和注意力机制能够有效捕捉多尺度特征。EVA-02通过遮蔽图像建模进行强大的预训练，在捕获全局特征方面表现出色。 在模型微调后，我们冻结这些模型的参数，并为分类头重新进行训练。最后，采用多数投票集成方法来结合各模型的预测结果，以增强系统的稳健性和对未见场景的泛化能力。所提出的系统能够应对真实世界条件下的深度伪造检测挑战，在验证数据集上达到了95.83%的良好准确率。
URL
https://arxiv.org/abs/2501.16704
