# Complement Face Forensic Detection and Localization with FacialLandmarks

arXiv: https://arxiv.org/abs/1910.05455 · HTML: https://mlsanity.com/paper/1910.05455/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [Generalizing Face Forgery Detection with High-frequency Features](https://mlsanity.com/paper/2103.12376/) (2021)
- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [Detect and Locate: Exposing Face Manipulation by Semantic- and Noise-level Telltales](https://mlsanity.com/paper/2107.05821/) (2021)
- [MaLP: Manipulation Localization Using a Proactive Scheme](https://mlsanity.com/paper/2303.16976/) (2023)
- [Multi-spectral Class Center Network for Face Manipulation Detection and Localization](https://mlsanity.com/paper/2305.10794/) (2023)
- [Metric Learning for Anti-Compression Facial Forgery Detection](https://mlsanity.com/paper/2103.08397/) (2021)
- [An Architecture for the detection of GAN-generated Flood Images with Localization Capabilities](https://mlsanity.com/paper/2205.07073/) (2022)
- [When Handcrafted Features and Deep Features Meet Mismatched Training and Test Sets for Deepfake Detection](https://mlsanity.com/paper/2209.13289/) (2022)

## Abstract (arXiv)

Recently, Generative Adversarial Networks (GANs) and image manipulating methods are becoming more powerful and can produce highly realistic face images beyond human recognition which have raised significant concerns regarding the authenticity of digital media. Although there have been some prior works that tackle face forensic classification problem, it is not trivial to estimate edited locations from classification predictions. In this paper, we propose, to the best of our knowledge, the first rigorous face forensic localization dataset, which consists of genuine, generated, and manipulated face images. In particular, the pristine parts contain face images from CelebA and FFHQ datasets. The fake images are generated from various GANs methods, namely DCGANs, LSGANs, BEGANs, WGAN-GP, ProGANs, and StyleGANs. Lastly, the edited subset is generated from StarGAN and SEFCGAN based on free-form masks. In total, the dataset contains about 1.3 million facial images labelled with corresponding binary masks. Based on the proposed dataset, we demonstrated that explicit adding facial landmarks information in addition to input images improves the performance. In addition, our proposed method consists of two branches and can coherently predict face forensic detection and localization to outperform the previous state-of-the-art techniques on the newly proposed dataset as well as the faceforecsic++ dataset especially on low-quality videos.