# AI-Generated Image Detection using a Cross-Attention Enhanced Dual-Stream Network

arXiv: https://arxiv.org/abs/2306.07005 · HTML: https://mlsanity.com/paper/2306.07005/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Text-to-image Diffusion Models in Generative AI: A Survey](https://mlsanity.com/paper/2303.07909/) (2023)
- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [Universal Deep Network for Steganalysis of Color Image based on Channel Representation](https://mlsanity.com/paper/2111.12231/) (2021)
- [Distinguishing Computer-generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning](https://mlsanity.com/paper/1803.09403/) (2018)

## Built on by (most cited first, 22 of 22)

- [Detecting Multimedia Generated by Large AI Models: A Survey](https://mlsanity.com/paper/2402.00045/) (2024)
- [GenDet: Towards Good Generalizations for AI-Generated Image Detection](https://mlsanity.com/paper/2312.08880/) (2023)
- [Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios](https://mlsanity.com/paper/2509.09172/) (2025)
- [ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization](https://mlsanity.com/paper/2505.11003/) (2025)
- [Bi-LORA: A Vision-Language Approach for Synthetic Image Detection](https://mlsanity.com/paper/2404.01959/) (2024)
- [Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images](https://mlsanity.com/paper/2502.11989/) (2025)
- [Venus-DeFakerOne: Unified Fake Image Detection & Localization](https://mlsanity.com/paper/2605.14091/) (2026)
- [BC4LLM: Trusted Artificial Intelligence When Blockchain Meets Large Language Models](https://mlsanity.com/paper/2310.06278/) (2023)
- [Artifact Feature Purification for Cross-domain Detection of AI-generated Images](https://mlsanity.com/paper/2403.11172/) (2024)
- [Improving Interpretability and Robustness for the Detection of AI-Generated Images](https://mlsanity.com/paper/2406.15035/) (2024)
- [Robust AI-Generated Face Detection with Imbalanced Data](https://mlsanity.com/paper/2505.02182/) (2025)
- [The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking](https://mlsanity.com/paper/2404.14581/) (2024)
- [DA-HFNet: Progressive Fine-Grained Forgery Image Detection and Localization Based on Dual Attention](https://mlsanity.com/paper/2406.01489/) (2024)
- [The Cat and Mouse Game: The Ongoing Arms Race Between Diffusion Models and Detection Methods](https://mlsanity.com/paper/2410.18866/) (2024)
- [Present and Future Generalization of Synthetic Image Detectors](https://mlsanity.com/paper/2409.14128/) (2024)
- [Can We Build a Monolithic Model for Fake Image Detection? SICA: Semantic-Induced Constrained Adaptation for Unified-Yet-Discriminative Artifact Feature Space Reconstruction](https://mlsanity.com/paper/2602.06676/) (2026)
- [FIDAVL: Fake Image Detection and Attribution using Vision-Language Model](https://mlsanity.com/paper/2409.03109/) (2024)
- [A Difference-in-Difference Approach to Detecting AI-Generated Images](https://mlsanity.com/paper/2602.23732/) (2026)
- [FUSE: Unifying Spectral and Semantic Cues for Robust AI-Generated Image Detection](https://mlsanity.com/paper/2512.21695/) (2025)
- [Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Images](https://mlsanity.com/paper/2606.28510/) (2026)
- [LHSDet: High-Resolution AI-Generated Image Detection via Visual Question Answering](https://mlsanity.com/paper/2608.07863/) (2026)
- [AniPrO: Interpretable Anime Image Provenance Detection via Multi-Dimensional Semantic Reasoning](https://mlsanity.com/paper/2609.23345/) (2026)

## Abstract (arXiv)

With the rapid evolution of AI Generated Content (AIGC), forged images produced through this technology are inherently more deceptive and require less human intervention compared to traditional Computer-generated Graphics (CG). However, owing to the disparities between CG and AIGC, conventional CG detection methods tend to be inadequate in identifying AIGC-produced images. To address this issue, our research concentrates on the text-to-image generation process in AIGC. Initially, we first assemble two text-to-image databases utilizing two distinct AI systems, DALLE2 and DreamStudio. Aiming to holistically capture the inherent anomalies produced by AIGC, we develope a robust dual-stream network comprised of a residual stream and a content stream. The former employs the Spatial Rich Model (SRM) to meticulously extract various texture information from images, while the latter seeks to capture additional forged traces in low frequency, thereby extracting complementary information that the residual stream may overlook. To enhance the information exchange between these two streams, we incorporate a cross multi-head attention mechanism. Numerous comparative experiments are performed on both databases, and the results show that our detection method consistently outperforms traditional CG detection techniques across a range of image resolutions. Moreover, our method exhibits superior performance through a series of robustness tests and cross-database experiments. When applied to widely recognized traditional CG benchmarks such as SPL2018 and DsTok, our approach significantly exceeds the capabilities of other existing methods in the field of CG detection.