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| Title: | 深偽視頻/圖像的模型歸因
Model Attribution for Deepfake Videos/Images |
| Authors: | 汪新
Ahmad, Wasim |
| Contributors: | 彭彥璁
張原豪
Yan-Tsung Peng
Yuan-Hao Chang
汪新
Wasim Ahmad |
| Keywords: | 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) |
| Date: | 2025 |
| Issue Date: | 2025-07-01 14:27:32 (UTC+8) |
| Abstract: | 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. |
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| Description: | 博士
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 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0108761506 |
| Data Type: | thesis |
| Appears in Collections: | [社群網路與人智計算國際研究生博士學位學程(TIGP)] 學位論文
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