{"ID":2865177,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.22070","arxiv_id":"2509.22070","title":"SpecXNet: A Dual-Domain Convolutional Network for Robust Deepfake Detection","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}}}.","short_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 (S...","url_abs":"https://arxiv.org/abs/2509.22070","url_pdf":"https://arxiv.org/pdf/2509.22070v1","authors":"[\"Inzamamul Alam\",\"Md Tanvir Islam\",\"Simon S. Woo\"]","published":"2025-09-26T08:51:59Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\",\"Generative Adversarial Network\"]","has_code":false,"code_links":[{"ID":609245,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2865177,"paper_url":"https://arxiv.org/abs/2509.22070","paper_title":"SpecXNet: A Dual-Domain Convolutional Network for Robust Deepfake Detection","repo_url":"https://github.com/inzamamulDU/SpecXNet","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}