# Controllable Guide-Space for Generalizable Face Forgery Detection

arXiv: https://arxiv.org/abs/2307.14039 · HTML: https://mlsanity.com/paper/2307.14039/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 50 references that are in the map.
- First posted on arXiv 26 Jul 2023.

## Builds on (most cited first, 30 of 37)

- [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)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [SimMIM: A Simple Framework for Masked Image Modeling](https://mlsanity.com/paper/2111.09886/) (2021)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Face2Face: Real-time Face Capture and Reenactment of RGB Videos](https://mlsanity.com/paper/2007.14808/) (2020)
- [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)
- [Face X-ray for More General Face Forgery Detection](https://mlsanity.com/paper/1912.13458/) (2019)
- [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 Deepfakes with Self-Blended Images](https://mlsanity.com/paper/2204.08376/) (2022)
- [Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection](https://mlsanity.com/paper/2012.07657/) (2020)
- [Generalizing Face Forgery Detection with High-frequency Features](https://mlsanity.com/paper/2103.12376/) (2021)
- [On the Detection of Digital Face Manipulation](https://mlsanity.com/paper/1910.01717/) (2019)
- [Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain](https://mlsanity.com/paper/2103.01856/) (2021)
- [Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videos](https://mlsanity.com/paper/1810.11215/) (2018)
- [Learning Self-Consistency for Deepfake Detection](https://mlsanity.com/paper/2012.09311/) (2020)
- [Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection](https://mlsanity.com/paper/2203.12208/) (2022)
- [Two-branch Recurrent Network for Isolating Deepfakes in Videos](https://mlsanity.com/paper/2008.03412/) (2020)
- [Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection](https://mlsanity.com/paper/2103.09096/) (2021)
- [Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videos](https://mlsanity.com/paper/1906.06876/) (2019)
- [What shapes feature representations? Exploring datasets, architectures, and training](https://mlsanity.com/paper/2006.12433/) (2020)
- [Two-Stream Neural Networks for Tampered Face Detection](https://mlsanity.com/paper/1803.11276/) (2018)
- [Dual Path Networks](https://mlsanity.com/paper/1707.01629/) (2017)
- [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)
- [Dual Contrastive Learning for General Face Forgery Detection](https://mlsanity.com/paper/2112.13522/) (2021)
- [FrePGAN: Robust Deepfake Detection Using Frequency-level Perturbations](https://mlsanity.com/paper/2202.03347/) (2022)

## Built on by (most cited first, 21 of 21)

- [LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detection](https://mlsanity.com/paper/2401.13856/) (2024)
- [Deepfake Generation and Detection: A Benchmark and Survey](https://mlsanity.com/paper/2403.17881/) (2024)
- [Common Sense Reasoning for Deepfake Detection](https://mlsanity.com/paper/2402.00126/) (2024)
- [Diffusion Model-Based Video Editing: A Survey](https://mlsanity.com/paper/2407.07111/) (2024)
- [DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion](https://mlsanity.com/paper/2410.04372/) (2024)
- [FSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation Learning](https://mlsanity.com/paper/2412.12032/) (2024)
- [Towards a Universal Synthetic Video Detector: From Face or Background Manipulations to Fully AI-Generated Content](https://mlsanity.com/paper/2412.12278/) (2024)
- [Unified Frequency-Assisted Transformer Framework for Detecting and Grounding Multi-Modal Manipulation](https://mlsanity.com/paper/2309.09667/) (2023)
- [FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing](https://mlsanity.com/paper/2509.22412/) (2025)
- [Joint Physical-Digital Facial Attack Detection Via Simulating Spoofing Clues](https://mlsanity.com/paper/2404.08450/) (2024)
- [REVEAL: Robust Evolution of Vision-Language Models for Explainable AI-Video Detection](https://mlsanity.com/paper/2502.14994/) (2025)
- [Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges](https://mlsanity.com/paper/2406.06965/) (2024)
- [Selective Domain-Invariant Feature for Generalizable Deepfake Detection](https://mlsanity.com/paper/2403.12707/) (2024)
- [Practical Manipulation Model for Robust Deepfake Detection](https://mlsanity.com/paper/2506.05119/) (2025)
- [Where the Devil Hides: Deepfake Detectors Can No Longer Be Trusted](https://mlsanity.com/paper/2505.08255/) (2025)
- [Deepfake Detection via Knowledge Injection](https://mlsanity.com/paper/2503.02503/) (2025)
- [CorrDetail: Visual Detail Enhanced Self-Correction for Face Forgery Detection](https://mlsanity.com/paper/2507.05302/) (2025)
- [Patch-Discontinuity Mining for Generalized Deepfake Detection](https://mlsanity.com/paper/2512.22027/) (2025)
- [Individualized Deepfake Detection Exploiting Traces Due to Double Neural-Network Operations](https://mlsanity.com/paper/2312.08034/) (2023)
- [Beyond Flicker: Detecting Kinematic Inconsistencies for Generalizable Deepfake Video Detection](https://mlsanity.com/paper/2512.04175/) (2025)
- [Specialist-Generalist Fusion with Outcome-Supervised Rationales for Deepfake Detection](https://mlsanity.com/paper/2605.31192/) (2026)

## Abstract (arXiv)

Recent studies on face forgery detection have shown satisfactory performance for methods involved in training datasets, but are not ideal enough for unknown domains. This motivates many works to improve the generalization, but forgery-irrelevant information, such as image background and identity, still exists in different domain features and causes unexpected clustering, limiting the generalization. In this paper, we propose a controllable guide-space (GS) method to enhance the discrimination of different forgery domains, so as to increase the forgery relevance of features and thereby improve the generalization. The well-designed guide-space can simultaneously achieve both the proper separation of forgery domains and the large distance between real-forgery domains in an explicit and controllable manner. Moreover, for better discrimination, we use a decoupling module to weaken the interference of forgery-irrelevant correlations between domains. Furthermore, we make adjustments to the decision boundary manifold according to the clustering degree of the same domain features within the neighborhood. Extensive experiments in multiple in-domain and cross-domain settings confirm that our method can achieve state-of-the-art generalization.