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<p>The rapid advancement of generative models has profoundly transformed the field of digital content creation, bringing unprecedented opportunities for media generation. However, the widespread adoption of this technology has also led to the emergence of highly realistic fake facial images and videos, which pose significant threats to public trust and societal security. To address the challenges of deepfake detection, this paper proposes a novel method based on Spatial-Frequency Feature Integration (SFFI), which effectively identifies fake content by combining spatial and frequency features of images. Additionally, to tackle the issue of class imbalance in the datasets, we propose an Authenticity-Aware Margin Loss (AAML). This loss function dynamically adjusts the decision boundary to enhance the model’s ability to recognize minority class samples. The proposed method was trained and evaluated on four challenging datasets: FaceForensics++, Celeb-DF v1, Celeb-DF v2, and the DeepFake Detection Challenge Preview, and compared against ten state-of-the-art methods. Experimental results demonstrate that the proposed method consistently outperforms all existing approaches across all datasets.</p>

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SFIAD: Deepfake detection through spatial-frequency feature integration and dynamic margin optimization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月23日

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  • Yi Kou,
  • Peng Li,
  • Hongjiang Ma,
  • Jiliu Zhou,
  • Zhan ao Huang,
  • Xiaojie Li

摘要

The rapid advancement of generative models has profoundly transformed the field of digital content creation, bringing unprecedented opportunities for media generation. However, the widespread adoption of this technology has also led to the emergence of highly realistic fake facial images and videos, which pose significant threats to public trust and societal security. To address the challenges of deepfake detection, this paper proposes a novel method based on Spatial-Frequency Feature Integration (SFFI), which effectively identifies fake content by combining spatial and frequency features of images. Additionally, to tackle the issue of class imbalance in the datasets, we propose an Authenticity-Aware Margin Loss (AAML). This loss function dynamically adjusts the decision boundary to enhance the model’s ability to recognize minority class samples. The proposed method was trained and evaluated on four challenging datasets: FaceForensics++, Celeb-DF v1, Celeb-DF v2, and the DeepFake Detection Challenge Preview, and compared against ten state-of-the-art methods. Experimental results demonstrate that the proposed method consistently outperforms all existing approaches across all datasets.