# Detecting AI-Generated Images via Diffusion Snap-Back Reconstruction: A Forensic Approach

arXiv: https://arxiv.org/abs/2511.00352 · HTML: https://mlsanity.com/paper/2511.00352/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 1 Nov 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, 9 of 9)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [Towards Universal Fake Image Detectors that Generalize Across Generative Models](https://mlsanity.com/paper/2302.10174/) (2023)
- [A Survey on Generative Diffusion Model](https://mlsanity.com/paper/2209.02646/) (2022)
- [Diffusion Models, Image Super-Resolution And Everything: A Survey](https://mlsanity.com/paper/2401.00736/) (2024)
- [FIRE: Robust Detection of Diffusion-Generated Images via Frequency-Guided Reconstruction Error](https://mlsanity.com/paper/2412.07140/) (2024)
- [A Survey of Diffusion Based Image Generation Models: Issues and Their Solutions](https://mlsanity.com/paper/2308.13142/) (2023)
- [ManiFPT: Defining and Analyzing Fingerprints of Generative Models](https://mlsanity.com/paper/2402.10401/) (2024)
- [Improving Synthetically Generated Image Detection in Cross-Concept Settings](https://mlsanity.com/paper/2304.12053/) (2023)

## Abstract (arXiv)

The rapid advancement of generative image models has transformed digital media to the point where AI generated images can no longer be reliably distinguished from authentic photographs by human observers or many conventional detection methods. Modern text to image systems such as Stable Diffusion and DALL E can now generate images so realistic that they often appear completely natural, leaving little to no visible artifacts for traditional deepfake detectors to rely on. This challenge has practical consequences for misinformation control, institutional identity verification, and digital trust in political and legal contexts. Instead of searching for hidden pixel level traces, we take a different approach: we observe how an image responds when it is gently disturbed and reconstructed by a diffusion model. We call this behavior diffusion snap back. By tracking how perceptual similarity measures (LPIPS, SSIM, and PSNR) change across different reconstruction strengths, we capture compact and interpretable signals that reveal how closely an image aligns with the diffusion model's learned denoising behavior. Evaluated on a balanced dataset of 4,000 human and AI generated images, the proposed method achieves an AUROC of 0.993 under stratified five fold cross validation and 0.990 on a holdout split using only logistic regression. Initial robustness tests show that the method remains stable under common real world distortions such as image compression and added noise. Although our experiments were conducted using a single diffusion backbone, the results indicate that reconstruction behavior can serve as a reliable and scalable foundation for synthetic media detection as generative models continue to grow more realistic.