# TransForensics: Image Forgery Localization with Dense Self-Attention

arXiv: https://arxiv.org/abs/2108.03871 · HTML: https://mlsanity.com/paper/2108.03871/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 44 references that are in the map.
- First posted on arXiv 9 Aug 2021.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Face X-ray for More General Face Forgery Detection](https://mlsanity.com/paper/1912.13458/) (2019)
- [Deeply-Supervised Nets](https://mlsanity.com/paper/1409.5185/) (2014)
- [Cross-Modal Self-Attention Network for Referring Image Segmentation](https://mlsanity.com/paper/1904.04745/) (2019)
- [Learning Rich Features for Image Manipulation Detection](https://mlsanity.com/paper/1805.04953/) (2018)
- [Two-Stream Neural Networks for Tampered Face Detection](https://mlsanity.com/paper/1803.11276/) (2018)
- [Noiseprint: a CNN-based camera model fingerprint](https://mlsanity.com/paper/1808.08396/) (2018)
- [SPAN: Spatial Pyramid Attention Network forImage Manipulation Localization](https://mlsanity.com/paper/2009.00726/) (2020)
- [Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries](https://mlsanity.com/paper/1903.02495/) (2019)
- [Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/1708.00930/) (2017)
- [Localization of JPEG double compression through multi-domain convolutional neural networks](https://mlsanity.com/paper/1706.01788/) (2017)
- [Universal Image Manipulation Detection using Deep Siamese Convolutional Neural Network](https://mlsanity.com/paper/1808.06323/) (2018)
- [FOCAL: A Forgery Localization Framework based on Video Coding Self-Consistency](https://mlsanity.com/paper/2008.10454/) (2020)

## Built on by (most cited first, 26 of 26)

- [Explicit Visual Prompting for Low-Level Structure Segmentations](https://mlsanity.com/paper/2303.10883/) (2023)
- [IML-ViT: Benchmarking Image Manipulation Localization by Vision Transformer](https://mlsanity.com/paper/2307.14863/) (2023)
- [IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization](https://mlsanity.com/paper/2406.10580/) (2024)
- [Explicit Visual Prompting for Universal Foreground Segmentations](https://mlsanity.com/paper/2305.18476/) (2023)
- [DeCLIP: Decoding CLIP representations for deepfake localization](https://mlsanity.com/paper/2409.08849/) (2024)
- [Exploring Multi-view Pixel Contrast for General and Robust Image Forgery Localization](https://mlsanity.com/paper/2406.13565/) (2024)
- [CFL-Net: Image Forgery Localization Using Contrastive Learning](https://mlsanity.com/paper/2210.02182/) (2022)
- [Multi-spectral Class Center Network for Face Manipulation Detection and Localization](https://mlsanity.com/paper/2305.10794/) (2023)
- [M2SFormer: Multi-Spectral and Multi-Scale Attention with Edge-Aware Difficulty Guidance for Image Forgery Localization](https://mlsanity.com/paper/2506.20922/) (2025)
- [ReLoc: A Restoration-Assisted Framework for Robust Image Tampering Localization](https://mlsanity.com/paper/2211.03930/) (2022)
- [TrainFors: A Large Benchmark Training Dataset for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2308.05264/) (2023)
- [Progressive Feedback-Enhanced Transformer for Image Forgery Localization](https://mlsanity.com/paper/2311.08910/) (2023)
- [SAMwave: Wavelet-Driven Feature Enrichment for Effective Adaptation of Segment Anything Model](https://mlsanity.com/paper/2507.20186/) (2025)
- [Vision Transformer Based Video Hashing Retrieval for Tracing the Source of Fake Videos](https://mlsanity.com/paper/2112.08117/) (2021)
- [Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection](https://mlsanity.com/paper/2212.05667/) (2022)
- [A Multi-Stream Fusion Network for Image Splicing Localization](https://mlsanity.com/paper/2212.01128/) (2022)
- [A Noise and Edge extraction-based dual-branch method for Shallowfake and Deepfake Localization](https://mlsanity.com/paper/2409.00896/) (2024)
- [HRGR: Enhancing Image Manipulation Detection via Hierarchical Region-aware Graph Reasoning](https://mlsanity.com/paper/2410.21861/) (2024)
- [Detection of Synthetic Face Images: Accuracy, Robustness, Generalization](https://mlsanity.com/paper/2406.17547/) (2024)
- [M^3:Manipulation Mask Manufacturer for Arbitrary-Scale Super-Resolution Mask](https://mlsanity.com/paper/2407.03695/) (2024)
- [Dual-Branch Convolutional Framework for Spatial and Frequency-Based Image Forgery Detection](https://mlsanity.com/paper/2509.05281/) (2025)
- [CBDiff:Conditional Bernoulli Diffusion Models for Image Forgery Localization](https://mlsanity.com/paper/2510.19597/) (2025)
- [VAAS: Vision-Attention Anomaly Scoring for Image Manipulation Detection in Digital Forensics](https://mlsanity.com/paper/2512.15512/) (2025)
- [SARIF: Segment Anything for Robust Image Forensics](https://mlsanity.com/paper/2606.21108/) (2026)
- [GAP-SAM: A Global Artifact Prior for Generalizable AI-Generated Image Manipulation Localization](https://mlsanity.com/paper/2608.20929/) (2026)
- [SegWave: Wavelet-Driven Segmentation of Tampered Regions](https://mlsanity.com/paper/2608.30714/) (2026)

## Abstract (arXiv)

Nowadays advanced image editing tools and technical skills produce tampered images more realistically, which can easily evade image forensic systems and make authenticity verification of images more difficult. To tackle this challenging problem, we introduce TransForensics, a novel image forgery localization method inspired by Transformers. The two major components in our framework are dense self-attention encoders and dense correction modules. The former is to model global context and all pairwise interactions between local patches at different scales, while the latter is used for improving the transparency of the hidden layers and correcting the outputs from different branches. Compared to previous traditional and deep learning methods, TransForensics not only can capture discriminative representations and obtain high-quality mask predictions but is also not limited by tampering types and patch sequence orders. By conducting experiments on main benchmarks, we show that TransForensics outperforms the stateof-the-art methods by a large margin.