Facial forgery techniques are advancing rapidly, posing severe threats to public trust and information security while imposing higher demands on the continual adaptation of DeepFake detection models. Although continual learning enables models to adapt to emerging forgery methods, existing approaches still face two key bottlenecks. On the one hand, they lack sufficient feature representation capacity to handle increasingly diverse and complex forgery traces. On the other hand, continual adaptation to new forgery distributions leads to severe catastrophic forgetting of prior knowledge, which substantially degrades detection performance. To address these issues, we propose Face-D(^2)CL, a framework for facial DeepFake detection. It leverages multi-domain synergistic representation to fuse spatial and frequency-domain features, enabling comprehensive capture of diverse forgery traces. Additionally, it employs a dual continual learning mechanism that combines Real/Fake-aware Elastic Weight Consolidation (RF-EWC) and Domain-wise Orthogonal Gradient Constraint (D-OGC). RF-EWC distinguishes the parameter importance for real versus fake samples, while D-OGC ensures that updates to task-specific expert modules do not interfere with previously learned knowledge. This synergy allows the model to achieve a dynamic balance between robust anti-forgetting capabilities and agile adaptability to emerging facial forgery paradigms, all without relying on historical data replay. Extensive experiments demonstrate that our method surpasses current state-of-the-art (SOTA) approaches in both stability and plasticity, achieving a 60.7% relative reduction in the average detection error rate. On unseen forgery domains, it further improves the average detection AUC by 7.9% compared to the current SOTA method.
Figures & tables
Figure 1: The pipeline of the proposed framework.
Symbol
Description
xi
Input image
f
Feature
f^
Normalized feature
θ
Model parameters
g
Gradient
g~
Updated gradient after orthogonal projection
Table 1: Key notations.
Figure 2: Overall architecture of Face-D 2 CL. The input face image is processed by three parallel branches (Spatial, Wavelet, Fourier) with domain alignment. The aligned features are encoded by a shared CLIP encoder with domain-specific expert modules. The resulting features are fused for classification and cosine alignment with text prompts. During training, a dual continual learning mechanism (RF-EWC and D-OGC) regulates parameter updates to prevent forgetting.
Method
Venue
No-Replay
Stage
Protocol 1 (Dataset Incremental)
Protocol 2 (Forgery Type Incremental)
FF++
DFDCP
DFD
CDF2
Avg
Hybrid
FR
FS
EFS
Avg
Offline (non-incremental) methods
DFD-FCG
CVPR’25
/
Task1
0.997
-
-
-
0.997
0.994
-
-
-
0.994
Task2
0.995
0.957
-
-
0.976
0.985
0.952
-
-
0.967
Task3
0.995
0.942
0.963
-
0.967
0.976
0.959
0.743
-
0.893
Task4
0.995
0.941
0.960
0.934
0.956
0.975
0.977
0.673
0.999
0.906
Table 2: Performance comparison on dataset-incremental (left) and forgery-type incremental (right) protocols. Each method is shown across four stages, corresponding to the performance on seen datasets after each task. Results are reported in terms of accuracy (ACC) for Protocol 1 and area under the ROC curve (AUC) for Protocol 2. Best results are shown in bold.
Figure 3: Robustness comparison of different methods under unseen perturbations based on Protocol 1. Average AUC across all test sets is reported under four perturbation types, namely block-wise dropout, grid shuffle, Gaussian noise, and median blur, each evaluated at five intensity levels.
Method
DF40
UADFV
WildDeepfake
AA
DFD-FCG
0.688
0.810
0.673
0.724
DFFreq
0.668
0.645
0.681
0.665
DFIL
0.680
0.961
0.708
0.783
SUR-LID
0.773
0.873
0.692
0.779
SAIDO
0.780
0.905
0.705
0.797
Face-D 2 CL
0.846
0.971
0.812
0.876
Table 3: Generalization under Protocol 1 (AUC).
Appendix figures & tables2 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 4: Cross-branch CKA among Spatial, Wavelet (IDWT), and Fourier (IFFT) features (centered linear CKA; Spatial–Wavelet 0.17 , Spatial–Fourier 0.07 , Wavelet–Fourier 0.26 ).
Encoder
AA
AF
CLIP-ViT-L/14
0.985
0.006
ResNet-50
0.971
0.093
ResNet-101
0.975
0.122
ResNet-152
0.977
0.106
DINOv2-S/14
0.925
0.019
DINOv2-B/14
0.952
0.041
Appendix
Table 16: Backbone comparison on Protocol 1 (ACC).
The rapid advancement of deepfake generation techniques poses significant threats to public safety and causes societal harm through the creation of highly realistic synthetic facial media. While existing detection methods demonstrate limitations in generalizing to emerging forgery patterns, this paper presents Deepfake Forensics Adapter (DFA), a novel dual-stream framework that synergizes vision-language foundation models with targeted forensics analysis. Our approach integrates a pre-trained CLIP model with three core components to achieve specialized deepfake detection by leveraging the powerful general capabilities of CLIP without changing CLIP parameters: 1) A Global Feature Adapter is used to identify global inconsistencies in image content that may indicate forgery, 2) A Local Anomaly Stream enhances the model's ability to perceive local facial forgery cues by explicitly leveraging facial structure priors, and 3) An Interactive Fusion Classifier promotes deep interaction and fusion between global and local features using a transformer encoder. Extensive evaluations of frame-level and video-level benchmarks demonstrate the superior generalization capabilities of DFA, particularly achieving state-of-the-art performance in the challenging DFDC dataset with frame-level AUC/EER of 0.816/0.256 and video-level AUC/EER of 0.836/0.251, representing a 4.8% video AUC improvement over previous methods. Our framework not only demonstrates state-of-the-art performance, but also points out a feasible and effective direction for developing a robust deepfake detection system with enhanced generalization capabilities against the evolving deepfake threats. Our code is available at https://github.com/Liao330/DFA.git
Jianfeng Liao, Yichen Wei, Raymond Chan Ching Bon +3
Shenzhen Technology University, Guangdong, China · Singapore Institute of Technology, Singapore, Singapore · The University of Hong Kong, Hong Kong, China +1
The rapid evolution of face forgery techniques has introduced an increasing variety of manipulations. Incremental Face Forgery Detection (IFFD), which incrementally adds new forgery data to fine-tune previously trained models, has emerged as a promising approach to handle evolving forgery threats. However, conventional replay-based IFFD methods suffer from catastrophic forgetting. Storing full historical images under limited memory often either fails to preserve subtle forgery cues or introduces domain bias, reducing the model's ability to learn intrinsic and transferable manipulation characteristics. In this paper, we propose a Density-Aware Regional Decisive replay strategy, termed InfoDense, to address these challenges. InfoDense prioritizes artifact-dense and forgery-critical regions, significantly reducing storage requirements while maintaining high-fidelity forgery evidence. We first introduce InfoDense Cut to localize decisive patches using CLIP-based embeddings. Then, InfoDense Select ranks candidate segments by combining latent-space representativeness and decisive patch counts, ensuring both diversity and information density in the replay buffer. Finally, InfoDense Fuse reconstructs unbiased training inputs by adaptively merging stored segments with current-task samples, enhancing knowledge retention and generalization. Extensive experiments on challenging incremental deepfake benchmarks demonstrate that InfoDense effectively mitigates catastrophic forgetting while improving cross-domain generalization.
Jikang Cheng, Hao Shen, Xueyi Zhang +4
2Peking University · 1Huazhong Agricultural University · 3National University of Singapore +2
Continual face forgery detection (CFFD) requires detectors to learn emerging forgery paradigms without forgetting previously seen manipulations. Existing CFFD methods commonly rely on replaying a small amount of past data to mitigate forgetting. Such replay is typically implemented either by storing a few historical samples or by synthesizing pseudo-forgeries from detector-dependent perturbations. Under strict memory budgets, the former cannot adequately cover diverse forgery cues and may expose facial identities, while the latter remains strongly tied to past decision boundaries. We argue that the core role of replay in CFFD is to reinstate the distributions of previous forgery tasks during subsequent training. To this end, we directly condense the discrepancy between real and fake distributions and leverage real faces from the current stage to perform distribution-level replay. Specifically, we introduce Distribution-Discrepancy Condensation (DDC), which models the real-to-fake discrepancy via a surrogate factorization in characteristic-function space and condenses it into a tiny bank of distribution discrepancy maps. We further propose Manifold-Consistent Replay (MCR), which synthesizes replay samples through variance-preserving composition of these maps with current-stage real faces, yielding samples that reflect previous-task forgery cues while remaining compatible with current real-face statistics. Operating under an extremely small memory budget and without directly storing raw historical face images, our framework consistently outperforms prior CFFD baselines and significantly mitigates catastrophic forgetting. Replay-level privacy analysis further suggests reduced identity leakage risk relative to selection-based replay.