# Fake It till You Make It: Curricular Dynamic Forgery Augmentations towards General Deepfake Detection

arXiv: https://arxiv.org/abs/2409.14444 · HTML: https://mlsanity.com/paper/2409.14444/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 66 references that are in the map.
- First posted on arXiv 22 Sep 2024.

## Builds on (most cited first, 30 of 44)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [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)
- [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)
- [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)
- [Exposing Deep Fakes Using Inconsistent Head Poses](https://mlsanity.com/paper/1811.00661/) (2018)
- [Generalizing Face Forgery Detection with High-frequency Features](https://mlsanity.com/paper/2103.12376/) (2021)
- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain](https://mlsanity.com/paper/2103.01856/) (2021)
- [The Deepfake Detection Challenge (DFDC) Preview Dataset](https://mlsanity.com/paper/1910.08854/) (2019)
- [WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection](https://mlsanity.com/paper/2101.01456/) (2021)
- [UCF: Uncovering Common Features for Generalizable Deepfake Detection](https://mlsanity.com/paper/2304.13949/) (2023)
- [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)
- [DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection](https://mlsanity.com/paper/2307.01426/) (2023)
- [Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond](https://mlsanity.com/paper/2101.11517/) (2021)
- [Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection](https://mlsanity.com/paper/2103.09096/) (2021)
- [Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection](https://mlsanity.com/paper/2311.11278/) (2023)
- [Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization](https://mlsanity.com/paper/2210.14457/) (2022)
- [Dual Contrastive Learning for General Face Forgery Detection](https://mlsanity.com/paper/2112.13522/) (2021)
- [TALL: Thumbnail Layout for Deepfake Video Detection](https://mlsanity.com/paper/2307.07494/) (2023)

## Built on by (most cited first, 30 of 31)

- [Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection](https://mlsanity.com/paper/2411.15633/) (2024)
- [Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection with Artifact Explanation](https://mlsanity.com/paper/2503.14905/) (2025)
- [X2-DFD: A framework for eXplainable and eXtendable Deepfake Detection](https://mlsanity.com/paper/2410.06126/) (2024)
- [From Specificity to Generality: Revisiting Generalizable Artifacts in Detecting Face Deepfakes](https://mlsanity.com/paper/2504.04827/) (2025)
- [Survey on AI-Generated Media Detection: From Non-MLLM to MLLM](https://mlsanity.com/paper/2502.05240/) (2025)
- [Guard Me If You Know Me: Protecting Specific Face-Identity from Deepfakes](https://mlsanity.com/paper/2505.19582/) (2025)
- [DeepShield: Fortifying Deepfake Video Detection with Local and Global Forgery Analysis](https://mlsanity.com/paper/2510.25237/) (2025)
- [Agent4FaceForgery: Multi-Agent LLM Framework for Realistic Face Forgery Detection](https://mlsanity.com/paper/2509.12546/) (2025)
- [AuViRe: Audio-visual Speech Representation Reconstruction for Deepfake Temporal Localization](https://mlsanity.com/paper/2511.18993/) (2025)
- [Explainable Deepfake Detection with RL Enhanced Self-Blended Images](https://mlsanity.com/paper/2601.15624/) (2026)
- [AuthGuard: Generalizable Deepfake Detection via Language Guidance](https://mlsanity.com/paper/2506.04501/) (2025)
- [Improving Deepfake Detection with Reinforcement Learning-Based Adaptive Data Augmentation](https://mlsanity.com/paper/2511.07051/) (2025)
- [FakeRadar: Probing Forgery Outliers to Detect Unknown Deepfake Videos](https://mlsanity.com/paper/2512.14601/) (2025)
- [When Detectors Forget Forensics: Blocking Semantic Shortcuts for Generalizable AI-Generated Image Detection](https://mlsanity.com/paper/2603.09242/) (2026)
- [LOGER: Local--Global Ensemble for Robust Deepfake Detection in the Wild](https://mlsanity.com/paper/2604.03558/) (2026)
- [Detecting AI-Generated Video: A Vision-Language Dual-View Survey](https://mlsanity.com/paper/2607.10787/) (2026)
- [SPARED: Reasoning-Based AI-Generated Image Detection via Adversarially Edited Data](https://mlsanity.com/paper/2608.12876/) (2026)
- [Leveraging Unlabeled Data from Unknown Sources via Dual-Path Guidance for Deepfake Face Detection](https://mlsanity.com/paper/2508.09022/) (2025)
- [R2M: Real-Aware Residual Model Merging for Robust and Generalizable Deepfake Detection](https://mlsanity.com/paper/2509.24367/) (2025)
- [Beyond Flicker: Detecting Kinematic Inconsistencies for Generalizable Deepfake Video Detection](https://mlsanity.com/paper/2512.04175/) (2025)
- [Low-rank Orthogonal Subspace Intervention for Generalizable Face Forgery Detection](https://mlsanity.com/paper/2601.11915/) (2026)
- [Tutor-Student Reinforcement Learning: A Dynamic Curriculum for Robust Deepfake Detection](https://mlsanity.com/paper/2603.24139/) (2026)
- [Unleashing Vision-Language Semantics for Deepfake Video Detection](https://mlsanity.com/paper/2603.24454/) (2026)
- [Generalizable Face Forgery Detection via Separable Prompt Learning](https://mlsanity.com/paper/2604.17307/) (2026)
- [Rethinking Cross-Domain Evaluation for Face Forgery Detection with Semantic Fine-grained Alignment and Mixture-of-Experts](https://mlsanity.com/paper/2604.21478/) (2026)
- [Exposing and Mitigating Temporal Attack in Deepfake Video Detection](https://mlsanity.com/paper/2605.07398/) (2026)
- [The Alpha Blending Hypothesis: Compositing Shortcut in Deepfake Detection](https://mlsanity.com/paper/2605.10334/) (2026)
- [Environment-Invariant Subspace Learning for Generalizable Deepfake Detection](https://mlsanity.com/paper/2608.17700/) (2026)
- [DBCF: Dual-Branch Complementary Fusion of Foundation Models for Generalized Deepfake Detection](https://mlsanity.com/paper/2609.34720/) (2026)
- [DF-CBM: Region-Aware Concept Bottleneck Models for Deepfake Detection](https://mlsanity.com/paper/2609.35096/) (2026)

## Abstract (arXiv)

Previous studies in deepfake detection have shown promising results when testing face forgeries from the same dataset as the training. However, the problem remains challenging when one tries to generalize the detector to forgeries from unseen datasets and created by unseen methods. In this work, we present a novel general deepfake detection method, called Curricular Dynamic Forgery Augmentation (CDFA), which jointly trains a deepfake detector with a forgery augmentation policy network. Unlike the previous works, we propose to progressively apply forgery augmentations following a monotonic curriculum during the training. We further propose a dynamic forgery searching strategy to select one suitable forgery augmentation operation for each image varying between training stages, producing a forgery augmentation policy optimized for better generalization. In addition, we propose a novel forgery augmentation named self-shifted blending image to simply imitate the temporal inconsistency of deepfake generation. Comprehensive experiments show that CDFA can significantly improve both cross-datasets and cross-manipulations performances of various naive deepfake detectors in a plug-and-play way, and make them attain superior performances over the existing methods in several benchmark datasets.