SpecXNet: A Dual-Domain Convolutional Network for Robust Deepfake Detection

• URL: http://arxiv.org/abs/2509.22070v1
• Date: Fri, 26 Sep 2025 08:51:59 GMT
• Title: SpecXNet: A Dual-Domain Convolutional Network for Robust Deepfake Detection
• Authors: Inzamamul Alam, Md Tanvir Islam, Simon S. Woo,
• Abstract summary: We propose the Spectral Cross-Attentional Network (SpecXNet), a dual-domain architecture for robust deepfake detection.<n>Built atop a modified XceptionNet backbone, we embed the DDFC and DFA modules within a separable convolution block.<n>Our results highlight the effectiveness of unified spatial-spectral learning for robust and general deepfake detection.
• Score: 25.04992532067041
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing realism of content generated by GANs and diffusion models has made deepfake detection significantly more challenging. Existing approaches often focus solely on spatial or frequency-domain features, limiting their generalization to unseen manipulations. We propose the Spectral Cross-Attentional Network (SpecXNet), a dual-domain architecture for robust deepfake detection. The core \textbf{Dual-Domain Feature Coupler (DDFC)} decomposes features into a local spatial branch for capturing texture-level anomalies and a global spectral branch that employs Fast Fourier Transform to model periodic inconsistencies. This dual-domain formulation allows SpecXNet to jointly exploit localized detail and global structural coherence, which are critical for distinguishing authentic from manipulated images. We also introduce the \textbf{Dual Fourier Attention (DFA)} module, which dynamically fuses spatial and spectral features in a content-aware manner. Built atop a modified XceptionNet backbone, we embed the DDFC and DFA modules within a separable convolution block. Extensive experiments on multiple deepfake benchmarks show that SpecXNet achieves state-of-the-art accuracy, particularly under cross-dataset and unseen manipulation scenarios, while maintaining real-time feasibility. Our results highlight the effectiveness of unified spatial-spectral learning for robust and generalizable deepfake detection. To ensure reproducibility, we released the full code on \href{https://github.com/inzamamulDU/SpecXNet}{\textcolor{blue}{\textbf{GitHub}}}.

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