DF-Net: The Digital Forensics Network for Image Forgery Detection
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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.
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
5 of 21 references.
References · 5
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2020Media Forensics and DeepFakes: an overviewcited by 209
- 2018Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blockscited by 76
- 2019Forensic Similarity for Digital Imagescited by 27
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.