# IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image Manipulation Detection & Localization

arXiv: https://arxiv.org/abs/2406.10580 · HTML: https://mlsanity.com/paper/2406.10580/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 49 references that are in the map.
- First posted on arXiv 15 Jun 2024.

## Builds on (most cited first, 26 of 26)

- [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)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [On the detection of synthetic images generated by diffusion models](https://mlsanity.com/paper/2211.00680/) (2022)
- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [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)
- [Hierarchical Fine-Grained Image Forgery Detection and Localization](https://mlsanity.com/paper/2303.17111/) (2023)
- [PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2103.10596/) (2021)
- [TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization](https://mlsanity.com/paper/2212.10957/) (2022)
- [MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection](https://mlsanity.com/paper/2112.08935/) (2021)
- [Image Manipulation Detection by Multi-View Multi-Scale Supervision](https://mlsanity.com/paper/2104.06832/) (2021)
- [Deep Learning for Deepfakes Creation and Detection: A Survey](https://mlsanity.com/paper/1909.11573/) (2019)
- [SPAN: Spatial Pyramid Attention Network forImage Manipulation Localization](https://mlsanity.com/paper/2009.00726/) (2020)
- [Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2108.12947/) (2021)
- [ObjectFormer for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2203.14681/) (2022)
- [TransForensics: Image Forgery Localization with Dense Self-Attention](https://mlsanity.com/paper/2108.03871/) (2021)
- [Pre-training-free Image Manipulation Localization through Non-Mutually Exclusive Contrastive Learning](https://mlsanity.com/paper/2309.14900/) (2023)
- [Personal Privacy Protection via Irrelevant Faces Tracking and Pixelation in Video Live Streaming](https://mlsanity.com/paper/2101.01060/) (2021)

## Built on by (most cited first, 30 of 42)

- [Mesoscopic Insights: Orchestrating Multi-scale & Hybrid Architecture for Image Manipulation Localization](https://mlsanity.com/paper/2412.13753/) (2024)
- [Ivy-Fake: A Unified Explainable Framework and Benchmark for Image and Video AIGC Detection](https://mlsanity.com/paper/2506.00979/) (2025)
- [Can We Get Rid of Handcrafted Feature Extractors? SparseViT: Nonsemantics-Centered, Parameter-Efficient Image Manipulation Localization through Spare-Coding Transformer](https://mlsanity.com/paper/2412.14598/) (2024)
- [ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization](https://mlsanity.com/paper/2505.11003/) (2025)
- [Zooming In on Fakes: A Novel Dataset for Localized AI-Generated Image Detection with Forgery Amplification Approach](https://mlsanity.com/paper/2504.11922/) (2025)
- [RAM: Recover Any 3D Human Motion in-the-Wild](https://mlsanity.com/paper/2603.19929/) (2026)
- [Fake-HR1: Rethinking Reasoning of Vision Language Model for Synthetic Image Detection](https://mlsanity.com/paper/2602.10042/) (2026)
- [Venus-DeFakerOne: Unified Fake Image Detection & Localization](https://mlsanity.com/paper/2605.14091/) (2026)
- [Mixture-of-Noises Enhanced Forgery-Aware Predictor for Multi-Face Manipulation Detection and Localization](https://mlsanity.com/paper/2408.02306/) (2024)
- [Omni-IML: Towards Unified Image Manipulation Localization](https://mlsanity.com/paper/2411.14823/) (2024)
- [AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era](https://mlsanity.com/paper/2504.11015/) (2025)
- [Beyond Fully Supervised Pixel Annotations: Scribble-Driven Weakly-Supervised Framework for Image Manipulation Localization](https://mlsanity.com/paper/2507.13018/) (2025)
- [Revisiting Image Manipulation Localization under Realistic Manipulation Scenarios](https://mlsanity.com/paper/2509.20006/) (2025)
- [GS-Checker: Tampering Localization for 3D Gaussian Splatting](https://mlsanity.com/paper/2511.20354/) (2025)
- [Can We Build a Monolithic Model for Fake Image Detection? SICA: Semantic-Induced Constrained Adaptation for Unified-Yet-Discriminative Artifact Feature Space Reconstruction](https://mlsanity.com/paper/2602.06676/) (2026)
- [From Passive Perception to Active Memory: A Weakly Supervised Image Manipulation Localization Framework Driven by Coarse-Grained Annotations](https://mlsanity.com/paper/2511.20359/) (2025)
- [HRGR: Enhancing Image Manipulation Detection via Hierarchical Region-aware Graph Reasoning](https://mlsanity.com/paper/2410.21861/) (2024)
- [Bridging Semantic Logic Gaps: A Cognition Inspired Multimodal Boundary Preserving Network for Image Manipulation Localization](https://mlsanity.com/paper/2508.07216/) (2025)
- [SurFITR: A Dataset for Surveillance Image Forgery Detection and Localisation](https://mlsanity.com/paper/2604.07101/) (2026)
- [Impostor: An Agent-Curated Benchmark for Realistic AIGC Manipulation Localization](https://mlsanity.com/paper/2606.04545/) (2026)
- [M^3:Manipulation Mask Manufacturer for Arbitrary-Scale Super-Resolution Mask](https://mlsanity.com/paper/2407.03695/) (2024)
- [RelayFormer: A Unified Local-Global Attention Framework for Scalable Image and Video Manipulation Localization](https://mlsanity.com/paper/2508.09459/) (2025)
- [EfficientIML: Efficient High-Resolution Image Manipulation Localization](https://mlsanity.com/paper/2509.08583/) (2025)
- [Shallow- and Deep-fake Image Manipulation Localization Using Vision Mamba and Guided Graph Neural Network](https://mlsanity.com/paper/2601.02566/) (2026)
- [DiffFace-Edit: A Diffusion-Based Facial Dataset for Forgery-Semantic Driven Deepfake Detection Analysis](https://mlsanity.com/paper/2601.13551/) (2026)
- [The Courtroom Trial of Pixels: Robust Image Manipulation Localization via Adversarial Evidence and Reinforcement Learning Judgment](https://mlsanity.com/paper/2604.14703/) (2026)
- [DINOv3 Beats Specialized Detectors: A Simple Foundation Model Baseline for Image Forensics](https://mlsanity.com/paper/2604.16083/) (2026)
- [EDGER: EDge-Guided with HEatmap Refinement for Generalizable Image Forgery Localization](https://mlsanity.com/paper/2605.12002/) (2026)
- [Towards Generalized Image Manipulation Localization via Score-based Model](https://mlsanity.com/paper/2605.16879/) (2026)
- [DocQT: Improving Document Forgery Localization Robustness via Diverse JPEG Quantization Tables](https://mlsanity.com/paper/2605.19688/) (2026)

## Abstract (arXiv)

A comprehensive benchmark is yet to be established in the Image Manipulation Detection & Localization (IMDL) field. The absence of such a benchmark leads to insufficient and misleading model evaluations, severely undermining the development of this field. However, the scarcity of open-sourced baseline models and inconsistent training and evaluation protocols make conducting rigorous experiments and faithful comparisons among IMDL models challenging. To address these challenges, we introduce IMDL-BenCo, the first comprehensive IMDL benchmark and modular codebase. IMDL-BenCo: i) decomposes the IMDL framework into standardized, reusable components and revises the model construction pipeline, improving coding efficiency and customization flexibility; ii) fully implements or incorporates training code for state-of-the-art models to establish a comprehensive IMDL benchmark; and iii) conducts deep analysis based on the established benchmark and codebase, offering new insights into IMDL model architecture, dataset characteristics, and evaluation standards. Specifically, IMDL-BenCo includes common processing algorithms, 8 state-of-the-art IMDL models (1 of which are reproduced from scratch), 2 sets of standard training and evaluation protocols, 15 GPU-accelerated evaluation metrics, and 3 kinds of robustness evaluation. This benchmark and codebase represent a significant leap forward in calibrating the current progress in the IMDL field and inspiring future breakthroughs. Code is available at: https://github.com/scu-zjz/IMDLBenCo.