# When Synthetic Traces Hide Real Content: Analysis of Stable Diffusion Image Laundering arXiv: https://arxiv.org/abs/2407.10736 ยท HTML: https://mlsanity.com/paper/2407.10736/ ## Key facts - Cited by 7 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 24 of its 30 references that are in the map. - First posted on arXiv 15 Jul 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, 24 of 24) - [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) - [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (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) - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019) - [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023) - [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019) - [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020) - [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017) - [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016) - [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021) - [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019) - [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019) - [Adversarial Diffusion Distillation](https://mlsanity.com/paper/2311.17042/) (2023) - [One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing](https://mlsanity.com/paper/2011.15126/) (2020) - [DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models](https://mlsanity.com/paper/2210.14896/) (2022) - [On the detection of synthetic images generated by diffusion models](https://mlsanity.com/paper/2211.00680/) (2022) - [Raising the Bar of AI-generated Image Detection with CLIP](https://mlsanity.com/paper/2312.00195/) (2023) - [Noiseprint: a CNN-based camera model fingerprint](https://mlsanity.com/paper/1808.08396/) (2018) - [Detecting GAN-generated Images by Orthogonal Training of Multiple CNNs](https://mlsanity.com/paper/2203.02246/) (2022) - [Beyond Deepfake Images: Detecting AI-Generated Videos](https://mlsanity.com/paper/2404.15955/) (2024) - [Training CNNs in Presence of JPEG Compression: Multimedia Forensics vs Computer Vision](https://mlsanity.com/paper/2009.12088/) (2020) - [Hiding Local Manipulations on SAR Images: a Counter-Forensic Attack](https://mlsanity.com/paper/2407.07041/) (2024) ## Built on by (most cited first, 7 of 7) - [A Bias-Free Training Paradigm for More General AI-generated Image Detection](https://mlsanity.com/paper/2412.17671/) (2024) - [Circumventing shortcuts in audio-visual deepfake detection datasets with unsupervised learning](https://mlsanity.com/paper/2412.00175/) (2024) - [WILD: a new in-the-Wild Image Linkage Dataset for synthetic image attribution](https://mlsanity.com/paper/2504.19595/) (2025) - [Is JPEG AI going to change image forensics?](https://mlsanity.com/paper/2412.03261/) (2024) - [Training-free Source Attribution of AI-generated Images via Resynthesis](https://mlsanity.com/paper/2510.24278/) (2025) - [Beyond Spectral Peaks: Interpreting the Cues Behind Synthetic Image Detection](https://mlsanity.com/paper/2510.05633/) (2025) - [Backbone is All You Need: Assessing Vulnerabilities of Frozen Foundation Models in Synthetic Image Forensics](https://mlsanity.com/paper/2605.13381/) (2026) ## Abstract (arXiv) In recent years, methods for producing highly realistic synthetic images have significantly advanced, allowing the creation of high-quality images from text prompts that describe the desired content. Even more impressively, Stable Diffusion (SD) models now provide users with the option of creating synthetic images in an image-to-image translation fashion, modifying images in the latent space of advanced autoencoders. This striking evolution, however, brings an alarming consequence: it is possible to pass an image through SD autoencoders to reproduce a synthetic copy of the image with high realism and almost no visual artifacts. This process, known as SD image laundering, can transform real images into lookalike synthetic ones and risks complicating forensic analysis for content authenticity verification. Our paper investigates the forensic implications of image laundering, revealing a serious potential to obscure traces of real content, including sensitive and harmful materials that could be mistakenly classified as synthetic, thereby undermining the protection of individuals depicted. To address this issue, we propose a two-stage detection pipeline that effectively differentiates between pristine, laundered, and fully synthetic images (those generated from text prompts), showing robustness across various conditions. Finally, we highlight another alarming property of image laundering, which appears to mask the unique artifacts exploited by forensic detectors to solve the camera model identification task, strongly undermining their performance. Our experimental code is available at https://github.com/polimi-ispl/synthetic-image-detection.