# FakeLocator: Robust Localization of GAN-Based Face Manipulations

arXiv: https://arxiv.org/abs/2001.09598 · HTML: https://mlsanity.com/paper/2001.09598/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2020.

## Built on by (most cited first, 30 of 40)

- [Hierarchical Fine-Grained Image Forgery Detection and Localization](https://mlsanity.com/paper/2303.17111/) (2023)
- [Representative Forgery Mining for Fake Face Detection](https://mlsanity.com/paper/2104.06609/) (2021)
- [TALL: Thumbnail Layout for Deepfake Video Detection](https://mlsanity.com/paper/2307.07494/) (2023)
- [Face Forensics in the Wild](https://mlsanity.com/paper/2103.16076/) (2021)
- [LEGION: Learning to Ground and Explain for Synthetic Image Detection](https://mlsanity.com/paper/2503.15264/) (2025)
- [Towards General Visual-Linguistic Face Forgery Detection](https://mlsanity.com/paper/2307.16545/) (2023)
- [Personalization as a Shortcut for Few-Shot Backdoor Attack against Text-to-Image Diffusion Models](https://mlsanity.com/paper/2305.10701/) (2023)
- [Evading DeepFake Detectors via Adversarial Statistical Consistency](https://mlsanity.com/paper/2304.11670/) (2023)
- [MaLP: Manipulation Localization Using a Proactive Scheme](https://mlsanity.com/paper/2303.16976/) (2023)
- [Detect Any Deepfakes: Segment Anything Meets Face Forgery Detection and Localization](https://mlsanity.com/paper/2306.17075/) (2023)
- [GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization](https://mlsanity.com/paper/2406.16531/) (2024)
- [Digital and Physical Face Attacks: Reviewing and One Step Further](https://mlsanity.com/paper/2209.14692/) (2022)
- [Language-guided Hierarchical Fine-grained Image Forgery Detection and Localization](https://mlsanity.com/paper/2410.23556/) (2024)
- [A Survey of Defenses Against AI-Generated Visual Media: Detection,Disruption, and Authentication](https://mlsanity.com/paper/2407.10575/) (2024)
- [Delocate: Detection and Localization for Deepfake Videos with Randomly-Located Tampered Traces](https://mlsanity.com/paper/2401.13516/) (2024)
- [Multi-spectral Class Center Network for Face Manipulation Detection and Localization](https://mlsanity.com/paper/2305.10794/) (2023)
- [Learning to Discover Forgery Cues for Face Forgery Detection](https://mlsanity.com/paper/2409.01030/) (2024)
- [Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook](https://mlsanity.com/paper/2411.19537/) (2024)
- [VideoFACT: Detecting Video Forgeries Using Attention, Scene Context, and Forensic Traces](https://mlsanity.com/paper/2211.15775/) (2022)
- [Fast Propagation is Better: Accelerating Single-Step Adversarial Training via Sampling Subnetworks](https://mlsanity.com/paper/2310.15444/) (2023)
- [Mixture-of-Noises Enhanced Forgery-Aware Predictor for Multi-Face Manipulation Detection and Localization](https://mlsanity.com/paper/2408.02306/) (2024)
- [Open Set Classification of GAN-based Image Manipulations via a ViT-based Hybrid Architecture](https://mlsanity.com/paper/2304.05212/) (2023)
- [DAM-GAN : Image Inpainting using Dynamic Attention Map based on Fake Texture Detection](https://mlsanity.com/paper/2204.09442/) (2022)
- [Exploring Spatial-Temporal Features for Deepfake Detection and Localization](https://mlsanity.com/paper/2210.15872/) (2022)
- [Passive Deepfake Detection Across Multi-modalities: A Comprehensive Survey](https://mlsanity.com/paper/2411.17911/) (2024)
- [FakeParts: a New Family of AI-Generated DeepFakes](https://mlsanity.com/paper/2508.21052/) (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)
- [Towards Generalizable Deepfake Detection by Primary Region Regularization](https://mlsanity.com/paper/2307.12534/) (2023)
- [Text Modality Oriented Image Feature Extraction for Detecting Diffusion-based DeepFake](https://mlsanity.com/paper/2405.18071/) (2024)

## Abstract (arXiv)

Full face synthesis and partial face manipulation by virtue of the generative adversarial networks (GANs) and its variants have raised wide public concerns. In the multi-media forensics area, detecting and ultimately locating the image forgery has become an imperative task. In this work, we investigate the architecture of existing GAN-based face manipulation methods and observe that the imperfection of upsampling methods therewithin could be served as an important asset for GAN-synthesized fake image detection and forgery localization. Based on this basic observation, we have proposed a novel approach, termed FakeLocator, to obtain high localization accuracy, at full resolution, on manipulated facial images. To the best of our knowledge, this is the very first attempt to solve the GAN-based fake localization problem with a gray-scale fakeness map that preserves more information of fake regions. To improve the universality of FakeLocator across multifarious facial attributes, we introduce an attention mechanism to guide the training of the model. To improve the universality of FakeLocator across different DeepFake methods, we propose partial data augmentation and single sample clustering on the training images. Experimental results on popular FaceForensics++, DFFD datasets and seven different state-of-the-art GAN-based face generation methods have shown the effectiveness of our method. Compared with the baselines, our method performs better on various metrics. Moreover, the proposed method is robust against various real-world facial image degradations such as JPEG compression, low-resolution, noise, and blur.