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題名 深偽視頻/圖像的模型歸因
Model Attribution for Deepfake Videos/Images作者 汪新
Ahmad, Wasim貢獻者 彭彥璁<br>張原豪
Yan-Tsung Peng<br>Yuan-Hao Chang
汪新
Wasim Ahmad關鍵詞 深偽技術
深偽模型歸屬（DFMA）
膠囊網路
動態路由演算法（DRA）
時空注意力機制（STA）
注意力機制
時間序列分析
人臉置換深偽
視訊鑑識
多媒體鑑識
資訊安全
生成對抗網路（GANs）
Deepfake
Deepfake Model Attribution (DFMA)
Capsule Networks
Dynamic Routing Algorithm (DRA)
Spatial-Temporal Attention (STA)
Attention Mechanism
Temporal Analysis
Face-swap Deepfakes
Video Forensics
Multimedia Forensics
Information Security
Generative Adversarial Networks (GANs)日期 2025 上傳時間 1-Jul-2025 14:27:32 (UTC+8) 摘要 隨著利用先進AI 換臉技術的Deepfake 影片數量迅速增加，建立能追溯影片生成來源的強健鑑識技術成為當前亟需解決的議題。不同於目前相關研究多聚焦於真假分類，模型歸屬（Model Attribution），亦即識別生成Deepfake 內容所使用之特定生成模型或工具，則提供了更細緻且具實際應用價值的途徑。透過揭示模型特有的生成痕跡，模型歸屬技術有助於溯源追查，同時支持針對性防禦策略的制定，以因應不斷演進的威脅。 本論文針對Deepfake 鑑識中的模型歸屬問題，將其建構為一個多分類任務。首先，提出Capsule-Spatial-Temporal（CapST）模型，此模型兼具輕量化與高效能特性。CapST 採用修改版VGG19 作為特徵提取骨幹，能有效萃取深層影像特徵；同時，結合膠囊網路（Capsule Networks）以捕捉複雜且層次化的特徵結構，並搭配時空注意力機制，進一步整合各影格間之時序資訊，進而達到穩健的模型歸屬效果。透過此設計，CapST 能顯著提升模型歸屬準確率，並於DFDM 與GANGen-Detection 資料集上具有優異表現，同時有效控制計算資源消耗，兼顧準確性與效能。 在此基礎上，本研究進一步提出FAME（Fake Attribution via Multi-level Embeddings）框架，旨在提升模型於多樣化資料與複雜影片條件下之泛化能力。相較於主要著重階層膠囊特徵之CapST，FAME 引入創新之多層次時空注意力策略，能敏銳偵測不同編碼解碼流程與壓縮設定下的細微生成痕跡。FAME 亦結合卷積神經網路（CNN）與長短期記憶網路（LSTM）生成混合空間-時間特徵嵌入，不僅提升歸屬準確率，亦有效降低模型參數量。 本研究於DFDM、FaceForensics++、FakeAVCeleb 及GANGen-Detection 等多個標竿資料集上進行全面性實驗評估。實驗結果顯示，CapST 能以低運算負擔達成高歸屬準確率，而FAME 更進一步於泛化能力、精確度及執行效率方面展現卓越表現。綜合而言，本論文提出之方法能有效應對生成式媒體技術所帶來的快速威脅演進，並提供一套具擴展性、精確且高效能的Deepfake 模型歸屬解決方案。
The rapid proliferation of Deepfake videos, enabled by sophisticated AI-driven face swapping techniques, has intensified the demand for robust forensic tools capable of identifying the generative sources behind these manipulations. While binary real/fake classification has been the primary focus in prior research, model attribution the task of determining the specific generative model or tool used to create a Deepfake—offers a more nuanced and actionable approach. By revealing model-specific artifacts, attribution facilitates source tracing and supports the development of tailored countermeasures against evolving threats. This dissertation addresses the model attribution problem in Deepfake forensics by casting it as a multiclass classification challenge. We first introduce the Capsule-Spatial-Temporal (CapST) model, a lightweight and effective framework, which leverages a truncated VGG19 for efficient feature extraction, Capsule Networks for hierarchical feature modeling, and a spatio-temporal attention mechanism to aggregate frame-level features into a robust video-level representation. The model demonstrates strong attribution performance on the DFDM and GANGen-Detection datasets while maintaining a compact computational footprint. Building on the strengths and limitations of CapST, we propose a second, more generalizable framework, FAME (Fake Attribution via Multi-level Embeddings). While CapST proved effective within controlled datasets, it was less optimized for attribution across diverse and challenging video conditions. FAME addresses this gap by introducing a novel multi-level spatio-temporal attention strategy, designed to detect subtle generative traces across different encoder-decoder pipelines and compression settings. Unlike CapST, which primarily emphasizes hierarchical capsule features, FAME incorporates hybrid spatial-temporal embeddings using CNNs and LSTMs, providing improved attribution accuracy with even fewer parameters. Both models have been extensively evaluated on benchmark datasets, including DFDM, FaceForensics ++, FakeAVCeleb, and GANGen detection. CapST showcases high attribution accuracy with low computational cost, while FAME further advances generalization, accuracy, and runtime efficiency across varied scenarios. Together, these contributions offer a comprehensive solution to Deepfake model attribution, paving the way for scalable and effective forensic applications that can adapt to the fast-evolving landscape of generative media technologies.參考文獻 [1] Darius Afchar et al. “MesoNet: A Compact Facial Video Forgery Detection Network”. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, 2018, pp. 1–7. 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國立政治大學
社群網路與人智計算國際研究生博士學位學程(TIGP)
108761506資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108761506 資料類型 thesis dc.contributor.advisor 彭彥璁<br>張原豪 zh_TW dc.contributor.advisor Yan-Tsung Peng<br>Yuan-Hao Chang en_US dc.contributor.author (Authors) 汪新 zh_TW dc.contributor.author (Authors) Wasim Ahmad en_US dc.creator (作者) 汪新 zh_TW dc.creator (作者) Ahmad, Wasim en_US dc.date (日期) 2025 en_US dc.date.accessioned 1-Jul-2025 14:27:32 (UTC+8) - dc.date.available 1-Jul-2025 14:27:32 (UTC+8) - dc.date.issued (上傳時間) 1-Jul-2025 14:27:32 (UTC+8) - dc.identifier (Other Identifiers) G0108761506 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/157711 - dc.description (描述) 博士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 社群網路與人智計算國際研究生博士學位學程(TIGP) zh_TW dc.description (描述) 108761506 zh_TW dc.description.abstract (摘要) 隨著利用先進AI 換臉技術的Deepfake 影片數量迅速增加，建立能追溯影片生成來源的強健鑑識技術成為當前亟需解決的議題。不同於目前相關研究多聚焦於真假分類，模型歸屬（Model Attribution），亦即識別生成Deepfake 內容所使用之特定生成模型或工具，則提供了更細緻且具實際應用價值的途徑。透過揭示模型特有的生成痕跡，模型歸屬技術有助於溯源追查，同時支持針對性防禦策略的制定，以因應不斷演進的威脅。 本論文針對Deepfake 鑑識中的模型歸屬問題，將其建構為一個多分類任務。首先，提出Capsule-Spatial-Temporal（CapST）模型，此模型兼具輕量化與高效能特性。CapST 採用修改版VGG19 作為特徵提取骨幹，能有效萃取深層影像特徵；同時，結合膠囊網路（Capsule Networks）以捕捉複雜且層次化的特徵結構，並搭配時空注意力機制，進一步整合各影格間之時序資訊，進而達到穩健的模型歸屬效果。透過此設計，CapST 能顯著提升模型歸屬準確率，並於DFDM 與GANGen-Detection 資料集上具有優異表現，同時有效控制計算資源消耗，兼顧準確性與效能。 在此基礎上，本研究進一步提出FAME（Fake Attribution via Multi-level Embeddings）框架，旨在提升模型於多樣化資料與複雜影片條件下之泛化能力。相較於主要著重階層膠囊特徵之CapST，FAME 引入創新之多層次時空注意力策略，能敏銳偵測不同編碼解碼流程與壓縮設定下的細微生成痕跡。FAME 亦結合卷積神經網路（CNN）與長短期記憶網路（LSTM）生成混合空間-時間特徵嵌入，不僅提升歸屬準確率，亦有效降低模型參數量。 本研究於DFDM、FaceForensics++、FakeAVCeleb 及GANGen-Detection 等多個標竿資料集上進行全面性實驗評估。實驗結果顯示，CapST 能以低運算負擔達成高歸屬準確率，而FAME 更進一步於泛化能力、精確度及執行效率方面展現卓越表現。綜合而言，本論文提出之方法能有效應對生成式媒體技術所帶來的快速威脅演進，並提供一套具擴展性、精確且高效能的Deepfake 模型歸屬解決方案。 zh_TW dc.description.abstract (摘要) The rapid proliferation of Deepfake videos, enabled by sophisticated AI-driven face swapping techniques, has intensified the demand for robust forensic tools capable of identifying the generative sources behind these manipulations. While binary real/fake classification has been the primary focus in prior research, model attribution the task of determining the specific generative model or tool used to create a Deepfake—offers a more nuanced and actionable approach. By revealing model-specific artifacts, attribution facilitates source tracing and supports the development of tailored countermeasures against evolving threats. This dissertation addresses the model attribution problem in Deepfake forensics by casting it as a multiclass classification challenge. We first introduce the Capsule-Spatial-Temporal (CapST) model, a lightweight and effective framework, which leverages a truncated VGG19 for efficient feature extraction, Capsule Networks for hierarchical feature modeling, and a spatio-temporal attention mechanism to aggregate frame-level features into a robust video-level representation. The model demonstrates strong attribution performance on the DFDM and GANGen-Detection datasets while maintaining a compact computational footprint. Building on the strengths and limitations of CapST, we propose a second, more generalizable framework, FAME (Fake Attribution via Multi-level Embeddings). While CapST proved effective within controlled datasets, it was less optimized for attribution across diverse and challenging video conditions. FAME addresses this gap by introducing a novel multi-level spatio-temporal attention strategy, designed to detect subtle generative traces across different encoder-decoder pipelines and compression settings. Unlike CapST, which primarily emphasizes hierarchical capsule features, FAME incorporates hybrid spatial-temporal embeddings using CNNs and LSTMs, providing improved attribution accuracy with even fewer parameters. Both models have been extensively evaluated on benchmark datasets, including DFDM, FaceForensics ++, FakeAVCeleb, and GANGen detection. CapST showcases high attribution accuracy with low computational cost, while FAME further advances generalization, accuracy, and runtime efficiency across varied scenarios. Together, these contributions offer a comprehensive solution to Deepfake model attribution, paving the way for scalable and effective forensic applications that can adapt to the fast-evolving landscape of generative media technologies. en_US dc.description.tableofcontents Acknowledgments i 中文摘要 ii Abstract iii 1 Introduction 1 1.1 Motivation . . 1 1.2 Problem Statement . . 2 1.3 Objectives . . 2 1.4 Contributions . . 2 1.4.1 CapST (Capsule-Spatial-Temporal Framework) . . 2 1.4.2 ADNN (Attention-Driven Neural Network) . . 3 1.4.3 Collective Impact . . 3 1.5 Structure of Dissertation . . 3 2 Background and Related Work 5 2.1 Deepfake Technology . . 5 2.2 Deepfake Datasets . . 8 2.3 Evaluation Metrics and Loss Functions . . 8 2.3.1 Metrics . .8 2.3.2 Loss Functions . .10 2.4 Deepfake Generation (Models/Architectures) . . 10 2.4.1 Variational Autoencoders (VAEs) . . 10 2.4.2 Generative Adversarial Networks (GANs) . . 12 2.4.3 Diffusion Models . . 13 2.4.4 Comparison of Deepfake Techniques and Models . . 15 2.5 Deepfake Detection . . 15 2.5.1 Spatial Domain . . 15 2.5.2 Temporal Domain . . 17 2.5.3 Frequency Domain . . 18 2.5.4 Data-Driven Forgery Detection . . 18 2.5.5 Detection Using CNN + LSTM . . 19 2.5.6 Detection Using CNN + Vision Transformer . . 21 2.6 Deepfake Model Attribution: Identifying the Source of Synthetic Media . . 22 2.6.1 GAN-Based Model Attribution . . 23 2.6.2 Limitations of GANs in DeepFake Video Generation . .23 2.6.3 DFAE Model Attribution: Challenges and Research Gaps . . . .24 2.6.4 Advancing Model Attribution for DeepFakes . . 24 2.7 Summary . . 25 3 Methodologies for Deepfake Model Attribution 26 3.1 CapST: Leveraging Capsule Networks and Temporal Attention for Accu- rate Model Attribution in Deepfake Videos . . 26 3.1.1 Introduction to CapST Deepfake Model Attribution . .26 3.1.2 Overview of CapST Architecture . . 26 3.1.3 Frame-Level Feature Extraction . . 27 3.1.4 Capsule Network for Spatial Hierarchy Learning . . 28 3.1.5 Child Capsule . . 28 3.1.6 Parent Capsule . . 29 3.1.7 Capsule Network for Spatial Hierarchy Learning . . 30 3.1.8 CapST Classification Layer . . 30 3.1.9 Model Training and Loss Function . . 31 3.1.10 Summary . . 31 3.2 FAME: Fine-Grained Attribution via Multi-level Attention for Deepfake Model Attribution . . 32 3.2.1 Fine-Grained Attribution via Multi-level Attention (FAME) Ar- chitecture . . 32 3.2.2 Loss Function and Training Strategy . . 34 3.2.3 Summary . .34 4 EXPERIMENTS AND RESULTS 35 4.1 Approach 1→ CapST . . 35 4.1.1 Datasets . . 35 4.1.2 Experimental Setting . . 36 4.1.3 Results and Evaluation . . 36 4.1.4 Comparison with existing methods on DFDM Dataset . .37 4.1.5 Comparison with existing methods on GANGen-Detection Dataset . . 37 4.1.6 Analysis of Grad-CAM Output Across Different DeepFake Techniques and Model Configurations . . 39 4.1.7 Limitations and Observations: . . 41 4.1.8 Ablation Study . . 41 4.1.9 Comparing Our Results to DMA-STA Reproduced Results . . . 42 4.1.10 Comparison with DMA-STA in terms of Computational Complexity . . 42 4.1.11 Comparison using different backbones . . 43 4.1.12 Comparison using different VGG Layers and Number of Frames . . 44 4.1.13 Summary . . 45 4.2 Approach 2→ FAME . . 45 4.2.1 Overview . . 45 4.2.2 Datasets . . 45 4.2.3 Implementation Environment . . 46 4.2.4 Experimental Setup . . 46 4.2.5 Comparison with Existing Methods on DFDM Dataset . .47 4.2.6 Comparison with DMA-STA Existing and Reproduced Results . . 49 4.2.7 Comparison with Existing Methods on FF++ Dataset . .50 4.2.8 Comparison with Existing Methods on FAVCeleb Dataset . . . . 51 4.2.9 Evaluation Metrics . . 52 4.2.10 Summary of FAME . .53 5 Conclusion 55 5.1 Summary of Our Findings . . 55 5.2 Key Contributions . . 55 5.2.1 Development of Novel Model Attribution Frameworks . . . . . 56 5.2.2 Comprehensive Evaluation Across Multiple Datasets . . . . . 56 5.2.3 Optimized and Resource-Efficient Solutions for Model Attribution . . 56 5.2.4 Forensic Implications and Real-World Applicability . . . . . 56 5.3 Limitations . . 56 5.4 Future Directions . . 56 5.4.1 Expanding to Other Manipulation Techniques . . 57 5.4.2 Improving Robustness Against Adversarial Attacks . .57 5.4.3 Real-Time Attribution in Low-Resource Environments . . . . . 57 5.4.4 Integration with Legal and Policy Frameworks . . 57 5.5 Final Remarks . . 57 Bibliography . . 58 Appendix Publications . . 68 zh_TW dc.format.extent 16210653 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108761506 en_US dc.subject (關鍵詞) 深偽技術 zh_TW dc.subject (關鍵詞) 深偽模型歸屬（DFMA） zh_TW dc.subject (關鍵詞) 膠囊網路 zh_TW dc.subject (關鍵詞) 動態路由演算法（DRA） zh_TW dc.subject (關鍵詞) 時空注意力機制（STA） zh_TW dc.subject (關鍵詞) 注意力機制 zh_TW dc.subject (關鍵詞) 時間序列分析 zh_TW dc.subject (關鍵詞) 人臉置換深偽 zh_TW dc.subject (關鍵詞) 視訊鑑識 zh_TW dc.subject (關鍵詞) 多媒體鑑識 zh_TW dc.subject (關鍵詞) 資訊安全 zh_TW dc.subject (關鍵詞) 生成對抗網路（GANs） zh_TW dc.subject (關鍵詞) Deepfake en_US dc.subject (關鍵詞) Deepfake Model Attribution (DFMA) en_US dc.subject (關鍵詞) Capsule Networks en_US dc.subject (關鍵詞) Dynamic Routing Algorithm (DRA) en_US dc.subject (關鍵詞) Spatial-Temporal Attention (STA) en_US dc.subject (關鍵詞) Attention Mechanism en_US dc.subject (關鍵詞) Temporal Analysis en_US dc.subject (關鍵詞) Face-swap Deepfakes en_US dc.subject (關鍵詞) Video Forensics en_US dc.subject (關鍵詞) Multimedia Forensics en_US dc.subject (關鍵詞) Information Security en_US dc.subject (關鍵詞) Generative Adversarial Networks (GANs) en_US dc.title (題名) 深偽視頻/圖像的模型歸因 zh_TW dc.title (題名) Model Attribution for Deepfake Videos/Images en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] Darius Afchar et al. “MesoNet: A Compact Facial Video Forgery Detection Network”. 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