# Addressing Vulnerabilities in AI-Image Detection: Challenges and Proposed Solutions

arXiv: https://arxiv.org/abs/2412.00073 · HTML: https://mlsanity.com/paper/2412.00073/

## 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 1 of its 18 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## 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, 1 of 1)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)

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

- [Data Safety: Synthetic Data Quality Analysis Using CIFAKE Dataset](https://mlsanity.com/paper/2607.12165/) (2026)

## Abstract (arXiv)

The rise of advanced AI models like Generative Adversarial Networks (GANs) and diffusion models such as Stable Diffusion has made the creation of highly realistic images accessible, posing risks of misuse in misinformation and manipulation. This study evaluates the effectiveness of convolutional neural networks (CNNs), as well as DenseNet architectures, for detecting AI-generated images. Using variations of the CIFAKE dataset, including images generated by different versions of Stable Diffusion, we analyze the impact of updates and modifications such as Gaussian blurring, prompt text changes, and Low-Rank Adaptation (LoRA) on detection accuracy. The findings highlight vulnerabilities in current detection methods and propose strategies to enhance the robustness and reliability of AI-image detection systems.