CapST: Leveraging Capsule Networks and Temporal Attention for Accurate Model Attribution in Deep-fake Videos
This addresses the need for source tracing and tailored countermeasures in deep-fake detection, though it appears incremental as it builds on existing methods like capsule networks and attention mechanisms.
The paper tackled the problem of attributing deep-fake videos to their specific generation models for forensic analysis, and the result was that the proposed CapST model outperformed baseline models in attribution accuracy while reducing computational cost.
Deep-fake videos, generated through AI face-swapping techniques, have gained significant attention due to their potential for impactful impersonation attacks. While most research focuses on real vs. fake detection, attributing a deep-fake to its specific generation model or encoder is vital for forensic analysis, enabling source tracing and tailored countermeasures. This enhances detection by leveraging model-specific artifacts and supports proactive defenses. We investigate the model attribution problem for deep-fake videos using two datasets: Deepfakes from Different Models (DFDM) and GANGen-Detection, both comprising deep-fake videos and GAN-generated images. We use only fake images from GANGen-Detection to align with DFDM's focus on attribution rather than binary classification. We formulate the task as a multiclass classification problem and introduce a novel Capsule-Spatial-Temporal (CapST) model that integrates a truncated VGG19 network for feature extraction, capsule networks for hierarchical encoding, and a spatio-temporal attention mechanism. Video-level fusion captures temporal dependencies across frames. Experiments on DFDM and GANGen-Detection show CapST outperforms baseline models in attribution accuracy while reducing computational cost.