arXiv 2606.142302026-06-15cs.CVcs.CL

面向不同生成器的可泛化深度伪造检测的多域特征融合框架

A Multi-Domain Feature Fusion Framework for Generalizable Deepfake Detection Across Different Generators

School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan（巴基斯坦国立科技大学电气工程与计算机科学学院）；Department of Communication, Quality Management and Information Systems, Mid Sweden University, Ostersund Campus, Sweden（瑞典中部大学通信、质量管理和信息系统系，厄斯特松德校区）；Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, Luleå, Sweden（瑞典吕勒奥理工大学计算机科学、电气与空间工程系）

Amna Amjid, Sana Qadir, Mehwish Fatima, Raja Khurram Shahzad

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AI总结：

提出SGFF-Net，融合空间、梯度和DWT频率表示，在双残差学习架构中实现跨生成器和跨范式的深度伪造检测，准确率提升至79.80%。

摘要：

深度伪造是人工生成的图像、音频或视频，威胁隐私、安全和信息完整性。检测此类内容对于打击虚假信息至关重要，因为最新模型能生成高度逼真的内容。虽然基于空间或频率的方法在基于生成对抗网络（GANs）的深度伪造上取得了良好的检测率，但它们往往难以处理最近的扩散模型生成的图像。特别是，现有方法很少利用互补的多域表示或系统地评估跨生成器的鲁棒性。为了解决这些挑战，我们提出了一种多域深度伪造检测框架SGFF-Net（空间-梯度-频率融合网络），该网络在双残差学习架构中集成了空间、梯度和基于DWT（离散小波变换）的频率表示。实验结果表明，SGFF-Net在数据集内评估中达到了98.95%的准确率，并在跨模型（70.46%）和跨范式（69.94%）设置中提升了性能。结合多源训练和数据增强进一步增强了鲁棒性，在跨模型评估中准确率从70.46%提升到79.80%，在跨范式评估中从69%提升到78%，在真实世界数据上从61.50%提升到75.80%。与单域检测器不同，SGFF-Net在空间、梯度和小波频率域中学习互补的取证线索，从而在跨生成器和跨范式评估中具有更强的鲁棒性。结果进一步表明，将多域表示与数据多样性和增强相结合，显著提高了泛化能力，为开发更可靠的深度伪造检测系统提供了实用见解。

英文摘要：

Deepfakes are artificially generated images, audio, or videos that threaten privacy, security, and information integrity. Detecting such content is crucial for countering disinformation, as the latest models generate highly realistic content. While spatial- or frequency-based approaches achieve good detection rates on Generative Adversarial Networks (GANs)-based generated deepfakes, they often struggle with recent diffusion model-generated images. In particular, existing approaches rarely exploit complementary multi-domain representations or systematically evaluate cross-generator robustness. To address these challenges, we propose a multi-domain deepfake detection framework called SGFF-Net (Spatial-Gradient-Frequency Fusion Network) that integrates spatial, gradient, and DWT (Discrete Wavelet Transform)-based frequency representations within a dual residual learning architecture. Experimental results show that the SGFF-Net achieves 98.95\% accuracy in intra-dataset evaluation and improves performance in both cross-model (70.46\%) and cross-paradigm (69.94\%) settings. Incorporating multi-source training and data augmentation further enhances robustness, increasing accuracy from 70.46\% to 79.80\% in cross-model evaluation, from 69\% to 78\% in cross-paradigm evaluation, and from 61.50\% to 75.80\% on real-world data. Unlike single-domain detectors, the SGFF-Net learns complementary forensic cues across spatial, gradient, and wavelet-frequency domains, resulting in greater robustness under cross-generator and cross-paradigm evaluation. The results further show that combining multi-domain representations with data diversity and augmentation substantially improves generalization, providing practical insights for developing more reliable deepfake detection systems.

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