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

arXiv: https://arxiv.org/abs/2509.22070 · HTML: https://mlsanity.com/paper/2509.22070/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 8 of 8)

- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image](https://mlsanity.com/paper/2306.08571/) (2023)
- [FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals](https://mlsanity.com/paper/1901.02212/) (2019)
- [Deep Learning for Deepfakes Creation and Detection: A Survey](https://mlsanity.com/paper/1909.11573/) (2019)
- [FrePGAN: Robust Deepfake Detection Using Frequency-level Perturbations](https://mlsanity.com/paper/2202.03347/) (2022)
- [All but One: Surgical Concept Erasing with Model Preservation in Text-to-Image Diffusion Models](https://mlsanity.com/paper/2312.12807/) (2023)
- [DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion](https://mlsanity.com/paper/2410.04372/) (2024)
- [Why Do Facial Deepfake Detectors Fail?](https://mlsanity.com/paper/2302.13156/) (2023)

## Built on by (most cited first, 5 of 5)

- [SpecGuard: Spectral Projection-based Advanced Invisible Watermarking](https://mlsanity.com/paper/2510.07302/) (2025)
- [Efficient Zero-Shot AI-Generated Image Detection](https://mlsanity.com/paper/2603.21619/) (2026)
- [Detecting Diffusion-generated Images via Dynamic Assembly Forests](https://mlsanity.com/paper/2604.09106/) (2026)
- [Suppressing Forgery-Specific Shortcuts for Generalizable Deepfake Detection](https://mlsanity.com/paper/2606.01843/) (2026)
- [ISPCloak: Weaponizing ISP for Optimization-Free Physical Camouflage against Deepfake Detectors](https://mlsanity.com/paper/2607.21897/) (2026)

## Abstract (arXiv)

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 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 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 hrefhttps://github.com/inzamamulDU/SpecXNettextcolorblueGitHub.