# OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wild

arXiv: https://arxiv.org/abs/2107.14480 · HTML: https://mlsanity.com/paper/2107.14480/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- Builds on 62 of its 95 references that are in the map.
- First posted on arXiv 30 Jul 2021.

## Builds on (most cited first, 30 of 62)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Interpreting the Latent Space of GANs for Semantic Face Editing](https://mlsanity.com/paper/1907.10786/) (2019)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [Face2Face: Real-time Face Capture and Reenactment of RGB Videos](https://mlsanity.com/paper/2007.14808/) (2020)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics](https://mlsanity.com/paper/1909.12962/) (2019)
- [Face X-ray for More General Face Forgery Detection](https://mlsanity.com/paper/1912.13458/) (2019)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [MesoNet: a Compact Facial Video Forgery Detection Network](https://mlsanity.com/paper/1809.00888/) (2018)
- [FSGAN: Subject Agnostic Face Swapping and Reenactment](https://mlsanity.com/paper/1908.05932/) (2019)
- [Exposing DeepFake Videos By Detecting Face Warping Artifacts](https://mlsanity.com/paper/1811.00656/) (2018)
- [Conditional Convolutions for Instance Segmentation](https://mlsanity.com/paper/2003.05664/) (2020)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [Exposing Deep Fakes Using Inconsistent Head Poses](https://mlsanity.com/paper/1811.00661/) (2018)
- [SOLO: Segmenting Objects by Locations](https://mlsanity.com/paper/1912.04488/) (2019)
- [Few-Shot Adversarial Learning of Realistic Neural Talking Head Models](https://mlsanity.com/paper/1905.08233/) (2019)

## Built on by (most cited first, 30 of 50)

- [TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization](https://mlsanity.com/paper/2212.10957/) (2022)
- [Protecting Celebrities from DeepFake with Identity Consistency Transformer](https://mlsanity.com/paper/2203.01318/) (2022)
- [LEGION: Learning to Ground and Explain for Synthetic Image Detection](https://mlsanity.com/paper/2503.15264/) (2025)
- [Glitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization](https://mlsanity.com/paper/2305.01979/) (2023)
- [GLFF: Global and Local Feature Fusion for AI-synthesized Image Detection](https://mlsanity.com/paper/2211.08615/) (2022)
- [MINTIME: Multi-Identity Size-Invariant Video Deepfake Detection](https://mlsanity.com/paper/2211.10996/) (2022)
- [Celeb-DF++: A Large-scale Challenging Video DeepFake Benchmark for Generalizable Forensics](https://mlsanity.com/paper/2507.18015/) (2025)
- [A Survey of Defenses Against AI-Generated Visual Media: Detection,Disruption, and Authentication](https://mlsanity.com/paper/2407.10575/) (2024)
- [ForgeryGPT: A Multimodal LLM for Interpretable Image Forgery Detection and Localization](https://mlsanity.com/paper/2410.10238/) (2024)
- [Forensics-Bench: A Comprehensive Forgery Detection Benchmark Suite for Large Vision Language Models](https://mlsanity.com/paper/2503.15024/) (2025)
- [OpenFake: An Open Dataset and Platform Toward Real-World Deepfake Detection](https://mlsanity.com/paper/2509.09495/) (2025)
- [Comprint: Image Forgery Detection and Localization using Compression Fingerprints](https://mlsanity.com/paper/2210.02227/) (2022)
- [DeePhy: On Deepfake Phylogeny](https://mlsanity.com/paper/2209.09111/) (2022)
- [Fusion Transformer with Object Mask Guidance for Image Forgery Analysis](https://mlsanity.com/paper/2403.12229/) (2024)
- [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)
- [The MeVer DeepFake Detection Service: Lessons Learnt from Developing and Deploying in the Wild](https://mlsanity.com/paper/2204.12816/) (2022)
- [Generating Attribution Reports for Manipulated Facial Images: A Dataset and Baseline](https://mlsanity.com/paper/2412.19685/) (2024)
- [Learning Real Facial Concepts for Independent Deepfake Detection](https://mlsanity.com/paper/2505.04460/) (2025)
- [The Art of Camouflage: Few-Shot Learning for Animal Detection and Segmentation](https://mlsanity.com/paper/2304.07444/) (2023)
- [COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection](https://mlsanity.com/paper/2308.01520/) (2023)
- [Exploiting Facial Relationships and Feature Aggregation for Multi-Face Forgery Detection](https://mlsanity.com/paper/2310.04845/) (2023)
- [So-Fake: Benchmarking and Explaining Social Media Image Forgery Detection](https://mlsanity.com/paper/2505.18660/) (2025)
- [1M-Deepfakes Detection Challenge](https://mlsanity.com/paper/2409.06991/) (2024)
- [Passive Deepfake Detection Across Multi-modalities: A Comprehensive Survey](https://mlsanity.com/paper/2411.17911/) (2024)
- [DDL: A Large-Scale Dataset for Deepfake Detection and Localization in Diversified Real-World Scenarios](https://mlsanity.com/paper/2506.23292/) (2025)
- [SEED: A Benchmark Dataset for Sequential Facial Attribute Editing with Diffusion Models](https://mlsanity.com/paper/2506.00562/) (2025)
- [Seeing Through Deepfakes: A Human-Inspired Framework for Multi-Face Detection](https://mlsanity.com/paper/2507.14807/) (2025)
- [DiffSeg30k: A Multi-Turn Diffusion Editing Benchmark for Localized AIGC Detection](https://mlsanity.com/paper/2511.19111/) (2025)
- [Robust Deepfake On Unrestricted Media: Generation And Detection](https://mlsanity.com/paper/2202.06228/) (2022)

## Abstract (arXiv)

The proliferation of deepfake media is raising concerns among the public and relevant authorities. It has become essential to develop countermeasures against forged faces in social media. This paper presents a comprehensive study on two new countermeasure tasks: multi-face forgery detection and segmentation in-the-wild. Localizing forged faces among multiple human faces in unrestricted natural scenes is far more challenging than the traditional deepfake recognition task. To promote these new tasks, we have created the first large-scale dataset posing a high level of challenges that is designed with face-wise rich annotations explicitly for face forgery detection and segmentation, namely OpenForensics. With its rich annotations, our OpenForensics dataset has great potentials for research in both deepfake prevention and general human face detection. We have also developed a suite of benchmarks for these tasks by conducting an extensive evaluation of state-of-the-art instance detection and segmentation methods on our newly constructed dataset in various scenarios. The dataset, benchmark results, codes, and supplementary materials will be publicly available on our project page: https://sites.google.com/view/ltnghia/research/openforensics