{"ID":2870455,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.13107","arxiv_id":"2509.13107","title":"Hierarchical Deep Fusion Framework for Multi-dimensional Facial Forgery Detection - The 2024 Global Deepfake Image Detection Challenge","abstract":"The proliferation of sophisticated deepfake technology poses significant challenges to digital security and authenticity. Detecting these forgeries, especially across a wide spectrum of manipulation techniques, requires robust and generalized models. This paper introduces the Hierarchical Deep Fusion Framework (HDFF), an ensemble-based deep learning architecture designed for high-performance facial forgery detection. Our framework integrates four diverse pre-trained sub-models, Swin-MLP, CoAtNet, EfficientNetV2, and DaViT, which are meticulously fine-tuned through a multi-stage process on the MultiFFDI dataset. By concatenating the feature representations from these specialized models and training a final classifier layer, HDFF effectively leverages their collective strengths. This approach achieved a final score of 0.96852 on the competition's private leaderboard, securing the 20th position out of 184 teams, demonstrating the efficacy of hierarchical fusion for complex image classification tasks.","short_abstract":"The proliferation of sophisticated deepfake technology poses significant challenges to digital security and authenticity. Detecting these forgeries, especially across a wide spectrum of manipulation techniques, requires robust and generalized models. This paper introduces the Hierarchical Deep Fusion Framework (HDFF),...","url_abs":"https://arxiv.org/abs/2509.13107","url_pdf":"https://arxiv.org/pdf/2509.13107v1","authors":"[\"Kohou Wang\",\"Huan Hu\",\"Xiang Liu\",\"Zezhou Chen\",\"Ping Chen\",\"Zhaoxiang Liu\",\"Shiguo Lian\"]","published":"2025-09-16T14:06:54Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false}