# M3Dsynth: A dataset of medical 3D images with AI-generated local manipulations

arXiv: https://arxiv.org/abs/2309.07973 · HTML: https://mlsanity.com/paper/2309.07973/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 24 references that are in the map.
- First posted on arXiv 14 Sep 2023.

## Builds on (most cited first, 16 of 16)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [On the detection of synthetic images generated by diffusion models](https://mlsanity.com/paper/2211.00680/) (2022)
- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization](https://mlsanity.com/paper/2212.10957/) (2022)
- [Intriguing properties of synthetic images: from generative adversarial networks to diffusion models](https://mlsanity.com/paper/2304.06408/) (2023)
- [Image Manipulation Detection by Multi-View Multi-Scale Supervision](https://mlsanity.com/paper/2104.06832/) (2021)
- [Diffusion Deformable Model for 4D Temporal Medical Image Generation](https://mlsanity.com/paper/2206.13295/) (2022)
- [Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network](https://mlsanity.com/paper/1711.08324/) (2017)
- [CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning](https://mlsanity.com/paper/1901.03597/) (2019)
- [Forensic Analysis of Synthetically Generated Western Blot Images](https://mlsanity.com/paper/2112.08739/) (2021)
- [Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models](https://mlsanity.com/paper/2104.02107/) (2021)
- [Machine Learning based Medical Image Deepfake Detection: A Comparative Study](https://mlsanity.com/paper/2109.12800/) (2021)

## Built on by (most cited first, 11 of 11)

- [SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model](https://mlsanity.com/paper/2412.04292/) (2024)
- [Detecting Multimedia Generated by Large AI Models: A Survey](https://mlsanity.com/paper/2402.00045/) (2024)
- [LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models](https://mlsanity.com/paper/2410.09732/) (2024)
- [AV-Deepfake1M++: A Large-Scale Audio-Visual Deepfake Benchmark with Real-World Perturbations](https://mlsanity.com/paper/2507.20579/) (2025)
- [Multiverse Through Deepfakes: The MultiFakeVerse Dataset of Person-Centric Visual and Conceptual Manipulations](https://mlsanity.com/paper/2506.00868/) (2025)
- [Localization of Synthetic Manipulations in Western Blot Images](https://mlsanity.com/paper/2408.13786/) (2024)
- [Auditing Sybil: Explaining Deep Lung Cancer Risk Prediction Through Generative Interventional Attributions](https://mlsanity.com/paper/2602.02560/) (2026)
- [Shifts in Doctors' Eye Movements Between Real and AI-Generated Medical Images](https://mlsanity.com/paper/2504.15007/) (2025)
- [From Masks to Pixels and Meaning: A New Taxonomy, Benchmark, and Metrics for VLM Image Tampering](https://mlsanity.com/paper/2603.20193/) (2026)
- [Rescind: Countering Image Misconduct in Biomedical Publications with Vision-Language and State-Space Modeling](https://mlsanity.com/paper/2601.08040/) (2026)
- [HexMIL: Hierarchical Attention MIL for Ante-Hoc Explainable Detection of AI-Manipulated CT Volumes](https://mlsanity.com/paper/2608.05101/) (2026)

## Abstract (arXiv)

The ability to detect manipulated visual content is becoming increasingly important in many application fields, given the rapid advances in image synthesis methods. Of particular concern is the possibility of modifying the content of medical images, altering the resulting diagnoses. Despite its relevance, this issue has received limited attention from the research community. One reason is the lack of large and curated datasets to use for development and benchmarking purposes. Here, we investigate this issue and propose M3Dsynth, a large dataset of manipulated Computed Tomography (CT) lung images. We create manipulated images by injecting or removing lung cancer nodules in real CT scans, using three different methods based on Generative Adversarial Networks (GAN) or Diffusion Models (DM), for a total of 8,577 manipulated samples. Experiments show that these images easily fool automated diagnostic tools. We also tested several state-of-the-art forensic detectors and demonstrated that, once trained on the proposed dataset, they are able to accurately detect and localize manipulated synthetic content, even when training and test sets are not aligned, showing good generalization ability. Dataset and code are publicly available at https://grip-unina.github.io/M3Dsynth/.