# FreqCross: A Multi-Modal Frequency-Spatial Fusion Network for Robust Detection of Stable Diffusion 3.5 Generated Images

arXiv: https://arxiv.org/abs/2507.02995 · HTML: https://mlsanity.com/paper/2507.02995/

## Key facts

- Cited by 1 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 18 of its 24 references that are in the map.
- First posted on arXiv 1 Jul 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, 18 of 18)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics](https://mlsanity.com/paper/1909.12962/) (2019)
- [Leveraging Frequency Analysis for Deep Fake Image Recognition](https://mlsanity.com/paper/2003.08685/) (2020)
- [Towards Universal Fake Image Detectors that Generalize Across Generative Models](https://mlsanity.com/paper/2302.10174/) (2023)
- [DIRE for Diffusion-Generated Image Detection](https://mlsanity.com/paper/2303.09295/) (2023)
- [Detecting Deepfakes with Self-Blended Images](https://mlsanity.com/paper/2204.08376/) (2022)
- [Detecting and Simulating Artifacts in GAN Fake Images](https://mlsanity.com/paper/1907.06515/) (2019)
- [On the detection of synthetic images generated by diffusion models](https://mlsanity.com/paper/2211.00680/) (2022)
- [Are GAN generated images easy to detect? A critical analysis of the state-of-the-art](https://mlsanity.com/paper/2104.02617/) (2021)
- [A Closer Look at Fourier Spectrum Discrepancies for CNN-generated Images Detection](https://mlsanity.com/paper/2103.17195/) (2021)
- [Any-Resolution AI-Generated Image Detection by Spectral Learning](https://mlsanity.com/paper/2411.19417/) (2024)
- [Frequency Masking for Universal Deepfake Detection](https://mlsanity.com/paper/2401.06506/) (2024)
- [Generalizable AI-Generated Image Detection Based on Fractal Self-Similarity in the Spectrum](https://mlsanity.com/paper/2503.08484/) (2025)

## Built on by (most cited first, 1 of 1)

- [Dual-Space Modality Consistency Learning for Universal Cross-Modal Re-Identification](https://mlsanity.com/paper/2608.06943/) (2026)

## Abstract (arXiv)

The rapid advancement of diffusion models, particularly Stable Diffusion 3.5, has enabled the generation of highly photorealistic synthetic images that pose significant challenges to existing detection methods. This paper presents FreqCross, a novel multi-modal fusion network that combines spatial RGB features, frequency domain artifacts, and radial energy distribution patterns to achieve robust detection of AI-generated images. Our approach leverages a three-branch architecture: (1) a ResNet-18 backbone for spatial feature extraction, (2) a lightweight CNN for processing 2D FFT magnitude spectra, and (3) a multi-layer perceptron for analyzing radial energy profiles. We introduce a novel radial energy distribution analysis that captures characteristic frequency artifacts inherent in diffusion-generated images, and fuse it with spatial and spectral cues via simple feature concatenation followed by a compact classification head. Extensive experiments on a dataset of 10,000 paired real (MS-COCO) and synthetic (Stable Diffusion 3.5) images demonstrate that FreqCross achieves 97.8% accuracy, outperforming state-of-the-art baselines by 5.2%. The frequency analysis further reveals that synthetic images exhibit distinct spectral signatures in the 0.1--0.4 normalised frequency range, providing theoretical foundation for our approach. Code and pre-trained models are publicly available to facilitate reproducible research.