# BioFors: A Large Biomedical Image Forensics Dataset

arXiv: https://arxiv.org/abs/2108.12961 · HTML: https://mlsanity.com/paper/2108.12961/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 56 references that are in the map.
- First posted on arXiv 30 Aug 2021.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models](https://mlsanity.com/paper/1505.04870/) (2015)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics](https://mlsanity.com/paper/1909.12962/) (2019)
- [DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection](https://mlsanity.com/paper/2001.03024/) (2020)
- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [Two-branch Recurrent Network for Isolating Deepfakes in Videos](https://mlsanity.com/paper/2008.03412/) (2020)
- [Learning Rich Features for Image Manipulation Detection](https://mlsanity.com/paper/1805.04953/) (2018)
- [Recurrent Convolutional Strategies for Face Manipulation Detection in Videos](https://mlsanity.com/paper/1905.00582/) (2019)
- [A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images](https://mlsanity.com/paper/1812.10366/) (2018)
- [FocalMix: Semi-Supervised Learning for 3D Medical Image Detection](https://mlsanity.com/paper/2003.09108/) (2020)
- [SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis](https://mlsanity.com/paper/2001.00704/) (2020)
- [Extracting Scientific Figures with Distantly Supervised Neural Networks](https://mlsanity.com/paper/1804.02445/) (2018)
- [Deep Multimodal Image-Repurposing Detection](https://mlsanity.com/paper/1808.06686/) (2018)
- [Deep Matching and Validation Network -- An End-to-End Solution to Constrained Image Splicing Localization and Detection](https://mlsanity.com/paper/1705.09765/) (2017)
- [Instance Segmentation of Biological Images Using Harmonic Embeddings](https://mlsanity.com/paper/1904.05257/) (2019)
- [A Data Driven Approach for Compound Figure Separation Using Convolutional Neural Networks](https://mlsanity.com/paper/1703.05105/) (2017)
- [Solo or Ensemble? Choosing a CNN Architecture for Melanoma Classification](https://mlsanity.com/paper/1904.12724/) (2019)
- [AIRD: Adversarial Learning Framework for Image Repurposing Detection](https://mlsanity.com/paper/1903.00788/) (2019)
- [Scientific Image Tampering Detection Based On Noise Inconsistencies: A Method And Datasets](https://mlsanity.com/paper/2001.07799/) (2020)

## Built on by (most cited first, 8 of 8)

- [Forensic Analysis of Synthetically Generated Western Blot Images](https://mlsanity.com/paper/2112.08739/) (2021)
- [TrainFors: A Large Benchmark Training Dataset for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2308.05264/) (2023)
- [MONet: Multi-scale Overlap Network for Duplication Detection in Biomedical Images](https://mlsanity.com/paper/2207.09107/) (2022)
- [Localization of Synthetic Manipulations in Western Blot Images](https://mlsanity.com/paper/2408.13786/) (2024)
- [Rescind: Countering Image Misconduct in Biomedical Publications with Vision-Language and State-Space Modeling](https://mlsanity.com/paper/2601.08040/) (2026)
- [BioTamperNet: Affinity-Guided State-Space Model Detecting Tampered Biomedical Images](https://mlsanity.com/paper/2602.01435/) (2026)
- [Can Image Splicing and Copy-Move Forgery Be Detected by the Same Model? Forensim: An Attention-Based State-Space Approach](https://mlsanity.com/paper/2602.10079/) (2026)
- [Forensic-Aware Continual Adaptation for Image Forgery Localization](https://mlsanity.com/paper/2609.38251/) (2026)

## Abstract (arXiv)

Research in media forensics has gained traction to combat the spread of misinformation. However, most of this research has been directed towards content generated on social media. Biomedical image forensics is a related problem, where manipulation or misuse of images reported in biomedical research documents is of serious concern. The problem has failed to gain momentum beyond an academic discussion due to an absence of benchmark datasets and standardized tasks. In this paper we present BioFors -- the first dataset for benchmarking common biomedical image manipulations. BioFors comprises 47,805 images extracted from 1,031 open-source research papers. Images in BioFors are divided into four categories -- Microscopy, Blot/Gel, FACS and Macroscopy. We also propose three tasks for forensic analysis -- external duplication detection, internal duplication detection and cut/sharp-transition detection. We benchmark BioFors on all tasks with suitable state-of-the-art algorithms. Our results and analysis show that existing algorithms developed on common computer vision datasets are not robust when applied to biomedical images, validating that more research is required to address the unique challenges of biomedical image forensics.