# DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery Clues

arXiv: https://arxiv.org/abs/2309.09526 · HTML: https://mlsanity.com/paper/2309.09526/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 54 of its 80 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 30 of 54)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://mlsanity.com/paper/1711.05769/) (2017)
- [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)
- [Leveraging Frequency Analysis for Deep Fake Image Recognition](https://mlsanity.com/paper/2003.08685/) (2020)
- [Face X-ray for More General Face Forgery Detection](https://mlsanity.com/paper/1912.13458/) (2019)
- [DER: Dynamically Expandable Representation for Class Incremental Learning](https://mlsanity.com/paper/2103.16788/) (2021)
- [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 Deep Fakes Using Inconsistent Head Poses](https://mlsanity.com/paper/1811.00661/) (2018)
- [Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection](https://mlsanity.com/paper/2012.07657/) (2020)
- [Rainbow Memory: Continual Learning with a Memory of Diverse Samples](https://mlsanity.com/paper/2103.17230/) (2021)
- [Generalizing Face Forgery Detection with High-frequency Features](https://mlsanity.com/paper/2103.12376/) (2021)
- [Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain](https://mlsanity.com/paper/2103.01856/) (2021)
- [SimSwap: An Efficient Framework For High Fidelity Face Swapping](https://mlsanity.com/paper/2106.06340/) (2021)
- [Semantic Drift Compensation for Class-Incremental Learning](https://mlsanity.com/paper/2004.00440/) (2020)

## Built on by (most cited first, 23 of 23)

- [Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection](https://mlsanity.com/paper/2403.01786/) (2024)
- [Stacking Brick by Brick: Aligned Feature Isolation for Incremental Face Forgery Detection](https://mlsanity.com/paper/2411.11396/) (2024)
- [FLTracer: Accurate Poisoning Attack Provenance in Federated Learning](https://mlsanity.com/paper/2310.13424/) (2023)
- [Continuous fake media detection: adapting deepfake detectors to new generative techniques](https://mlsanity.com/paper/2406.08171/) (2024)
- [SAIDO: Generalizable Detection of AI-Generated Images via Scene-Aware and Importance-Guided Dynamic Optimization in Continual Learning](https://mlsanity.com/paper/2512.00539/) (2025)
- [Passive Deepfake Detection Across Multi-modalities: A Comprehensive Survey](https://mlsanity.com/paper/2411.17911/) (2024)
- [Harnessing Frequency Spectrum Insights for Image Copyright Protection Against Diffusion Models](https://mlsanity.com/paper/2503.11071/) (2025)
- [DevFD: Developmental Face Forgery Detection by Learning Shared and Orthogonal LoRA Subspaces](https://mlsanity.com/paper/2509.19230/) (2025)
- [Generalizable and Adaptive Continual Learning Framework for AI-generated Image Detection](https://mlsanity.com/paper/2601.05580/) (2026)
- [A Sanity Check for Multi-In-Domain Face Forgery Detection in the Real World](https://mlsanity.com/paper/2512.04837/) (2025)
- [GRACE: Graph-Regularized Attentive Convolutional Entanglement with Laplacian Smoothing for Robust DeepFake Video Detection](https://mlsanity.com/paper/2406.19941/) (2024)
- [Think Twice before Adaptation: Improving Adaptability of DeepFake Detection via Online Test-Time Adaptation](https://mlsanity.com/paper/2505.18787/) (2025)
- [Unifying Locality of KANs and Feature Drift Compensation Projection for Data-free Replay based Continual Face Forgery Detection](https://mlsanity.com/paper/2508.03189/) (2025)
- [UMCL: Unimodal-generated Multimodal Contrastive Learning for Cross-compression-rate Deepfake Detection](https://mlsanity.com/paper/2511.18983/) (2025)
- [Face-D(^2)CL: Multi-Domain Synergistic Representation with Dual Continual Learning for Facial DeepFake Detection](https://mlsanity.com/paper/2604.08159/) (2026)
- [AIFIND: Artifact-Aware Interpreting Fine-Grained Alignment for Incremental Face Forgery Detection](https://mlsanity.com/paper/2604.16207/) (2026)
- [Revisiting Deepfake Detection: Chronological Continual Learning and the Limits of Generalization](https://mlsanity.com/paper/2509.07993/) (2025)
- [When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection](https://mlsanity.com/paper/2511.18436/) (2025)
- [Beyond Binary Classification: A Semi-supervised Approach to Generalized AI-generated Image Detection](https://mlsanity.com/paper/2511.19499/) (2025)
- [Towards Robust DeepFake Detection under Unstable Face Sequences: Adaptive Sparse Graph Embedding with Order-Free Representation and Explicit Laplacian Spectral Prior](https://mlsanity.com/paper/2512.07498/) (2025)
- [Unveiling Deepfakes: A Frequency-Aware Triple Branch Network for Deepfake Detection](https://mlsanity.com/paper/2604.17477/) (2026)
- [DECODE: Tackling Representation and Decision Degradation in Continual AI-Generated Image Detection](https://mlsanity.com/paper/2607.27882/) (2026)
- [Preserving Knowledge across Space and Time for Continual Video Deepfake Detection](https://mlsanity.com/paper/2609.03446/) (2026)

## Abstract (arXiv)

The malicious use and widespread dissemination of deepfake pose a significant crisis of trust. Current deepfake detection models can generally recognize forgery images by training on a large dataset. However, the accuracy of detection models degrades significantly on images generated by new deepfake methods due to the difference in data distribution. To tackle this issue, we present a novel incremental learning framework that improves the generalization of deepfake detection models by continual learning from a small number of new samples. To cope with different data distributions, we propose to learn a domain-invariant representation based on supervised contrastive learning, preventing overfit to the insufficient new data. To mitigate catastrophic forgetting, we regularize our model in both feature-level and label-level based on a multi-perspective knowledge distillation approach. Finally, we propose to select both central and hard representative samples to update the replay set, which is beneficial for both domain-invariant representation learning and rehearsal-based knowledge preserving. We conduct extensive experiments on four benchmark datasets, obtaining the new state-of-the-art average forgetting rate of 7.01 and average accuracy of 85.49 on FF++, DFDC-P, DFD, and CDF2. Our code is released at https://github.com/DeepFakeIL/DFIL.