# DF-Net: The Digital Forensics Network for Image Forgery Detection

arXiv: https://arxiv.org/abs/2503.22398 · HTML: https://mlsanity.com/paper/2503.22398/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 28 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks](https://mlsanity.com/paper/1808.08127/) (2018)
- [Forensic Similarity for Digital Images](https://mlsanity.com/paper/1902.04684/) (2019)

## Built on by (most cited first, 2 of 2)

- [When Seeing Is Not Believing -- A Benchmark for Search-Grounded Video Misinformation Detection](https://mlsanity.com/paper/2606.04098/) (2026)
- [VAAS: Vision-Attention Anomaly Scoring for Image Manipulation Detection in Digital Forensics](https://mlsanity.com/paper/2512.15512/) (2025)

## Abstract (arXiv)

The orchestrated manipulation of public opinion, particularly through manipulated images, often spread via online social networks (OSN), has become a serious threat to society. In this paper we introduce the Digital Forensics Net (DF-Net), a deep neural network for pixel-wise image forgery detection. The released model outperforms several state-of-the-art methods on four established benchmark datasets. Most notably, DF-Net's detection is robust against lossy image operations (e.g resizing, compression) as they are automatically performed by social networks.