# Hierarchical Forgery Classifier On Multi-modality Face Forgery Clues

arXiv: https://arxiv.org/abs/2212.14629 · HTML: https://mlsanity.com/paper/2212.14629/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 83 references that are in the map.
- First posted on arXiv 30 Dec 2022.

## Builds on (most cited first, 30 of 31)

- [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)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics](https://mlsanity.com/paper/1909.12962/) (2019)
- [Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues](https://mlsanity.com/paper/2007.09355/) (2020)
- [Leveraging Frequency Analysis for Deep Fake Image Recognition](https://mlsanity.com/paper/2003.08685/) (2020)
- [MesoNet: a Compact Facial Video Forgery Detection Network](https://mlsanity.com/paper/1809.00888/) (2018)
- [Multi-attentional Deepfake Detection](https://mlsanity.com/paper/2103.02406/) (2021)
- [Detecting and Simulating Artifacts in GAN Fake Images](https://mlsanity.com/paper/1907.06515/) (2019)
- [What makes fake images detectable? Understanding properties that generalize](https://mlsanity.com/paper/2008.10588/) (2020)
- [Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain](https://mlsanity.com/paper/2103.01856/) (2021)
- [WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection](https://mlsanity.com/paper/2101.01456/) (2021)
- [Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videos](https://mlsanity.com/paper/1810.11215/) (2018)
- [Fourier Spectrum Discrepancies in Deep Network Generated Images](https://mlsanity.com/paper/1911.06465/) (2019)
- [Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection](https://mlsanity.com/paper/2103.09096/) (2021)
- [ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis](https://mlsanity.com/paper/2103.05630/) (2021)
- [M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection](https://mlsanity.com/paper/2104.09770/) (2021)
- [Representative Forgery Mining for Fake Face Detection](https://mlsanity.com/paper/2104.06609/) (2021)
- [Local Relation Learning for Face Forgery Detection](https://mlsanity.com/paper/2105.02577/) (2021)
- [A Closer Look at Fourier Spectrum Discrepancies for CNN-generated Images Detection](https://mlsanity.com/paper/2103.17195/) (2021)
- [Fighting deepfakes by detecting GAN DCT anomalies](https://mlsanity.com/paper/2101.09781/) (2021)
- [Initiative Defense against Facial Manipulation](https://mlsanity.com/paper/2112.10098/) (2021)
- [Wavelet-Packets for Deepfake Image Analysis and Detection](https://mlsanity.com/paper/2106.09369/) (2021)
- [Self-supervised Transformer for Deepfake Detection](https://mlsanity.com/paper/2203.01265/) (2022)
- [FedForgery: Generalized Face Forgery Detection with Residual Federated Learning](https://mlsanity.com/paper/2210.09563/) (2022)
- [End-to-End Photo-Sketch Generation via Fully Convolutional Representation Learning](https://mlsanity.com/paper/1501.07180/) (2015)
- [A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization](https://mlsanity.com/paper/1703.04636/) (2017)

## Built on by (most cited first, 9 of 9)

- [DATA: Multi-Disentanglement based Contrastive Learning for Open-World Semi-Supervised Deepfake Attribution](https://mlsanity.com/paper/2505.04384/) (2025)
- [Progressive Feedback-Enhanced Transformer for Image Forgery Localization](https://mlsanity.com/paper/2311.08910/) (2023)
- [GazeForensics: DeepFake Detection via Gaze-guided Spatial Inconsistency Learning](https://mlsanity.com/paper/2311.07075/) (2023)
- [Next-Frame Feature Prediction for Multimodal Deepfake Detection and Temporal Localization](https://mlsanity.com/paper/2511.10212/) (2025)
- [Patch-Discontinuity Mining for Generalized Deepfake Detection](https://mlsanity.com/paper/2512.22027/) (2025)
- [Latent Spatiotemporal Adaptation for Generalized Face Forgery Video Detection](https://mlsanity.com/paper/2309.04795/) (2023)
- [Exploring the Adversarial Robustness of Face Forgery Detection with Decision-based Black-box Attacks](https://mlsanity.com/paper/2310.12017/) (2023)
- [Imperceptible Face Forgery Attack via Adversarial Semantic Mask](https://mlsanity.com/paper/2406.10887/) (2024)
- [Learning Unified Video and Image Representation for Video Face Forgery Detection](https://mlsanity.com/paper/2608.13064/) (2026)

## Abstract (arXiv)

Face forgery detection plays an important role in personal privacy and social security. With the development of adversarial generative models, high-quality forgery images become more and more indistinguishable from real to humans. Existing methods always regard as forgery detection task as the common binary or multi-label classification, and ignore exploring diverse multi-modality forgery image types, e.g. visible light spectrum and near-infrared scenarios. In this paper, we propose a novel Hierarchical Forgery Classifier for Multi-modality Face Forgery Detection (HFC-MFFD), which could effectively learn robust patches-based hybrid domain representation to enhance forgery authentication in multiple-modality scenarios. The local spatial hybrid domain feature module is designed to explore strong discriminative forgery clues both in the image and frequency domain in local distinct face regions. Furthermore, the specific hierarchical face forgery classifier is proposed to alleviate the class imbalance problem and further boost detection performance. Experimental results on representative multi-modality face forgery datasets demonstrate the superior performance of the proposed HFC-MFFD compared with state-of-the-art algorithms. The source code and models are publicly available at https://github.com/EdWhites/HFC-MFFD.