# Aligned Datasets Improve Detection of Latent Diffusion-Generated Images

arXiv: https://arxiv.org/abs/2410.11835 · HTML: https://mlsanity.com/paper/2410.11835/

## Key facts

- Cited by 36 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 38 of its 47 references that are in the map.
- First posted on arXiv 15 Oct 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, 30 of 38)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [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)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis](https://mlsanity.com/paper/2310.00426/) (2023)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://mlsanity.com/paper/2310.04378/) (2023)
- [Score-based Generative Modeling in Latent Space](https://mlsanity.com/paper/2106.05931/) (2021)
- [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)
- [On the detection of synthetic images generated by diffusion models](https://mlsanity.com/paper/2211.00680/) (2022)
- [What makes fake images detectable? Understanding properties that generalize](https://mlsanity.com/paper/2008.10588/) (2020)

## Built on by (most cited first, 30 of 36)

- [Dual Data Alignment Makes AI-Generated Image Detector Easier Generalizable](https://mlsanity.com/paper/2505.14359/) (2025)
- [Community Forensics: Using Thousands of Generators to Train Fake Image Detectors](https://mlsanity.com/paper/2411.04125/) (2024)
- [Stay-Positive: A Case for Ignoring Real Image Features in Fake Image Detection](https://mlsanity.com/paper/2502.07778/) (2025)
- [Brought a Gun to a Knife Fight: Modern VFM Baselines Outgun Specialized Detectors on In-the-Wild AI Image Detection](https://mlsanity.com/paper/2509.12995/) (2025)
- [Scaling Up AI-Generated Image Detection with Generator-Aware Prototypes](https://mlsanity.com/paper/2512.12982/) (2025)
- [Preserving Forgery Artifacts: AI-Generated Video Detection at Native Scale](https://mlsanity.com/paper/2604.04634/) (2026)
- [EvoGuard: An Extensible Agentic RL-based Framework for Practical and Evolving AI-Generated Image Detection](https://mlsanity.com/paper/2603.17343/) (2026)
- [PGC: Peak-Guided Calibration for Generalizable AI-Generated Image Detection](https://mlsanity.com/paper/2605.21207/) (2026)
- [So-Fake: Benchmarking and Explaining Social Media Image Forgery Detection](https://mlsanity.com/paper/2505.18660/) (2025)
- [Where Detectors Fail: Probing Generative Space for Generalizable AI-Generated Image Detection](https://mlsanity.com/paper/2605.24906/) (2026)
- [AlignGemini: Generalizable AI-Generated Image Detection Through Task-Model Alignment](https://mlsanity.com/paper/2512.06746/) (2025)
- [Diversity Matters: Dataset Diversification and Dual-Branch Network for Generalized AI-Generated Image Detection](https://mlsanity.com/paper/2603.27800/) (2026)
- [Do Transformations Reveal the Truth? Generative Residual Learning for Generalized AI-Generated Image Detection](https://mlsanity.com/paper/2607.08674/) (2026)
- [DailyBench: A Unified Benchmark for AI-Generated and Manipulated Images from Modern Generative Models](https://mlsanity.com/paper/2607.24016/) (2026)
- [Veritas++: Value-aware On-Policy Distillation for Perception-Enhanced AIGI Detection](https://mlsanity.com/paper/2607.27113/) (2026)
- [HEDGE: Heterogeneous Ensemble for Detection of AI-GEnerated Images in the Wild](https://mlsanity.com/paper/2604.03555/) (2026)
- [Frequency-Aware Semantic Fusion with Gated Injection for AI-generated Image Detection](https://mlsanity.com/paper/2604.27875/) (2026)
- [AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images](https://mlsanity.com/paper/2604.28177/) (2026)
- [Reduce the Artifacts Bias for More Generalizable AI-Generated Image Detection](https://mlsanity.com/paper/2605.14486/) (2026)
- [Video as Natural Augmentation: Towards Unified AI-Generated Image and Video Detection](https://mlsanity.com/paper/2605.21977/) (2026)
- [Spectral Tail Auxiliary Learning for AI-Generated Image Detection](https://mlsanity.com/paper/2605.22751/) (2026)
- [HydraPrompt: An Adaptive and Asymmetric Framework of Vision-Language Models for Synthetic Image Detection](https://mlsanity.com/paper/2605.26421/) (2026)
- [BIAS-ID: A Framework for Analyzing Transformation Biases in AI-Generated Image Detectors](https://mlsanity.com/paper/2605.31153/) (2026)
- [Dissect and Prune: Enhancing Robustness in AI-Generated Image Detection](https://mlsanity.com/paper/2606.10309/) (2026)
- [GlobalForge: Towards Robust AI-Generated Image Detection](https://mlsanity.com/paper/2607.14684/) (2026)
- [SPARED: Reasoning-Based AI-Generated Image Detection via Adversarially Edited Data](https://mlsanity.com/paper/2608.12876/) (2026)
- [Anchor-Regularized Adaptation for Generalizable AI-Generated Image Detection with DINOv3](https://mlsanity.com/paper/2608.15196/) (2026)
- [Transferable Dual-Domain Feature Importance Attack against AI-Generated Image Detector](https://mlsanity.com/paper/2511.15571/) (2025)
- [Micro-Defects Expose Macro-Fakes: Detecting AI-Generated Images via Local Distributional Shifts](https://mlsanity.com/paper/2605.09296/) (2026)
- [Test-Time Curriculum for Open-Set AIGC Detection](https://mlsanity.com/paper/2608.00559/) (2026)

## Abstract (arXiv)

As latent diffusion models (LDMs) democratize image generation capabilities, there is a growing need to detect fake images. A good detector should focus on the generative models fingerprints while ignoring image properties such as semantic content, resolution, file format, etc. Fake image detectors are usually built in a data driven way, where a model is trained to separate real from fake images. Existing works primarily investigate network architecture choices and training recipes. In this work, we argue that in addition to these algorithmic choices, we also require a well aligned dataset of real/fake images to train a robust detector. For the family of LDMs, we propose a very simple way to achieve this: we reconstruct all the real images using the LDMs autoencoder, without any denoising operation. We then train a model to separate these real images from their reconstructions. The fakes created this way are extremely similar to the real ones in almost every aspect (e.g., size, aspect ratio, semantic content), which forces the model to look for the LDM decoders artifacts. We empirically show that this way of creating aligned real/fake datasets, which also sidesteps the computationally expensive denoising process, helps in building a detector that focuses less on spurious correlations, something that a very popular existing method is susceptible to. Finally, to demonstrate just how effective the alignment in a dataset can be, we build a detector using images that are not natural objects, and present promising results. Overall, our work identifies the subtle but significant issues that arise when training a fake image detector and proposes a simple and inexpensive solution to address these problems.