# CFL-Net: Image Forgery Localization Using Contrastive Learning

arXiv: https://arxiv.org/abs/2210.02182 · HTML: https://mlsanity.com/paper/2210.02182/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 4 Oct 2022.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Generalizing Face Forgery Detection with High-frequency Features](https://mlsanity.com/paper/2103.12376/) (2021)
- [Exploring Cross-Image Pixel Contrast for Semantic Segmentation](https://mlsanity.com/paper/2101.11939/) (2021)
- [Learning Rich Features for Image Manipulation Detection](https://mlsanity.com/paper/1805.04953/) (2018)
- [Fighting Fake News: Image Splice Detection via Learned Self-Consistency](https://mlsanity.com/paper/1805.04096/) (2018)
- [Noiseprint: a CNN-based camera model fingerprint](https://mlsanity.com/paper/1808.08396/) (2018)
- [What makes instance discrimination good for transfer learning?](https://mlsanity.com/paper/2006.06606/) (2020)
- [Dual Contrastive Learning for General Face Forgery Detection](https://mlsanity.com/paper/2112.13522/) (2021)
- [SPAN: Spatial Pyramid Attention Network forImage Manipulation Localization](https://mlsanity.com/paper/2009.00726/) (2020)
- [Image Splicing Localization Using A Multi-Task Fully Convolutional Network (MFCN)](https://mlsanity.com/paper/1709.02016/) (2017)
- [Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries](https://mlsanity.com/paper/1903.02495/) (2019)
- [TransForensics: Image Forgery Localization with Dense Self-Attention](https://mlsanity.com/paper/2108.03871/) (2021)
- [DeepfakeUCL: Deepfake Detection via Unsupervised Contrastive Learning](https://mlsanity.com/paper/2104.11507/) (2021)
- [Localization of JPEG double compression through multi-domain convolutional neural networks](https://mlsanity.com/paper/1706.01788/) (2017)
- [Boundary-based Image Forgery Detection by Fast Shallow CNN](https://mlsanity.com/paper/1801.06732/) (2018)
- [L2-Constrained RemNet for Camera Model Identification and Image Manipulation Detection](https://mlsanity.com/paper/2009.05379/) (2020)

## Built on by (most cited first, 12 of 12)

- [IML-ViT: Benchmarking Image Manipulation Localization by Vision Transformer](https://mlsanity.com/paper/2307.14863/) (2023)
- [Exploring Multi-view Pixel Contrast for General and Robust Image Forgery Localization](https://mlsanity.com/paper/2406.13565/) (2024)
- [Rethinking Image Forgery Detection via Soft Contrastive Learning and Unsupervised Clustering](https://mlsanity.com/paper/2308.09307/) (2023)
- [M2SFormer: Multi-Spectral and Multi-Scale Attention with Edge-Aware Difficulty Guidance for Image Forgery Localization](https://mlsanity.com/paper/2506.20922/) (2025)
- [DA-HFNet: Progressive Fine-Grained Forgery Image Detection and Localization Based on Dual Attention](https://mlsanity.com/paper/2406.01489/) (2024)
- [InpDiffusion: Image Inpainting Localization via Conditional Diffusion Models](https://mlsanity.com/paper/2501.02816/) (2025)
- [Webly-Supervised Image Manipulation Localization via Category-Aware Auto-Annotation](https://mlsanity.com/paper/2508.20987/) (2025)
- [RecoverMark: Robust Watermarking for Localization and Recovery of Manipulated Faces](https://mlsanity.com/paper/2602.20618/) (2026)
- [Video Inpainting Localization with Contrastive Learning](https://mlsanity.com/paper/2406.17628/) (2024)
- [Semantic Manipulation Localization](https://mlsanity.com/paper/2604.10132/) (2026)
- [STLGRU: Spatio-Temporal Lightweight Graph GRU for Traffic Flow Prediction](https://mlsanity.com/paper/2212.04548/) (2022)
- [HRFNet: High-Resolution Forgery Network for Localizing Satellite Image Manipulation](https://mlsanity.com/paper/2307.11052/) (2023)

## Abstract (arXiv)

Conventional forgery localizing methods usually rely on different forgery footprints such as JPEG artifacts, edge inconsistency, camera noise, etc., with cross-entropy loss to locate manipulated regions. However, these methods have the disadvantage of over-fitting and focusing on only a few specific forgery footprints. On the other hand, real-life manipulated images are generated via a wide variety of forgery operations and thus, leave behind a wide variety of forgery footprints. Therefore, we need a more general approach for image forgery localization that can work well on a variety of forgery conditions. A key assumption in underlying forged region localization is that there remains a difference of feature distribution between untampered and manipulated regions in each forged image sample, irrespective of the forgery type. In this paper, we aim to leverage this difference of feature distribution to aid in image forgery localization. Specifically, we use contrastive loss to learn mapping into a feature space where the features between untampered and manipulated regions are well-separated for each image. Also, our method has the advantage of localizing manipulated region without requiring any prior knowledge or assumption about the forgery type. We demonstrate that our work outperforms several existing methods on three benchmark image manipulation datasets. Code is available at https://github.com/niloy193/CFLNet.