# Development of a Dual-Input Neural Model for Detecting AI-Generated Imagery

arXiv: https://arxiv.org/abs/2406.13688 · HTML: https://mlsanity.com/paper/2406.13688/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 12 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image](https://mlsanity.com/paper/2306.08571/) (2023)
- [Detecting and Simulating Artifacts in GAN Fake Images](https://mlsanity.com/paper/1907.06515/) (2019)
- [Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark](https://mlsanity.com/paper/2109.14545/) (2021)
- [Are GAN generated images easy to detect? A critical analysis of the state-of-the-art](https://mlsanity.com/paper/2104.02617/) (2021)
- [CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images](https://mlsanity.com/paper/2303.14126/) (2023)
- [Frequency learning for image classification](https://mlsanity.com/paper/2006.15476/) (2020)
- [Harnessing Machine Learning for Discerning AI-Generated Synthetic Images](https://mlsanity.com/paper/2401.07358/) (2024)

## Abstract (arXiv)

Over the past years, images generated by artificial intelligence have become more prevalent and more realistic. Their advent raises ethical questions relating to misinformation, artistic expression, and identity theft, among others. The crux of many of these moral questions is the difficulty in distinguishing between real and fake images. It is important to develop tools that are able to detect AI-generated images, especially when these images are too realistic-looking for the human eye to identify as fake. This paper proposes a dual-branch neural network architecture that takes both images and their Fourier frequency decomposition as inputs. We use standard CNN-based methods for both branches as described in Stuchi et al. [7], followed by fully-connected layers. Our proposed model achieves an accuracy of 94% on the CIFAKE dataset, which significantly outperforms classic ML methods and CNNs, achieving performance comparable to some state-of-the-art architectures, such as ResNet.