Context-Aware Deepfake Detection for Securing AI-Driven Financial Transactions
Changcun Liu, Guisheng Zhang, Siyou Guo, Qilei Li, Gwanggil Jeon, Mingliang Gao · IEEE Transactions on Computational Social Systems · 2025
The rapid advancement of deepfake technology has threatened the community’s sense of security, particularly in the context of face-based payment systems. Thus, deepfake detection has emerged as a critical issue demanding immediate attention. However, the generalization performance of existing detection models is limited as they are overly reliant on specific forged features while ignoring the common forged features. To address this problem, we introduce the context-aware decoupling network (CADNet) for deepfake detection. Specifically, a context self-calibration (CSC) module is constructed to guide the network to focus on local forged regions. It enlarges possible regions to increase the likelihood of forgery cues. Meanwhile, a frequency domain decoupling (FDD) module is introduced to extract and fuse different frequency components. It realizes the collaborative representation optimization of global semantics and local details. The experimental results prove that the proposed model exhibits strong generalization capability across multiple standard datasets. It achieves average area under the curve (AUC) values of 98.64% for in-domain evaluation and 75.52% for cross-dataset generalization.