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.
Figures & tables
Figure 1. Comparison of replay paradigms in CFFD. Selection-based replay uses sparse support samples and only coarsely approximates the previous fake distribution. Perturbation-based replay preserves boundary-related signals but ignores distribution alignment. Our method condenses the real-to-fake discrepancy and unfolds it through MCR in the next stage to produce replay samples that better match the previous fake distribution. Radial charts show a CF-based distribution measure; closer overlap means better replay fidelity.
Figure 2. Our method models the distribution discrepancy between real and fake samples via a surrogate factorization in characteristic-function (CF) space, and transforms it into compact discrepancy maps (DDMs) that can be composed with real faces to synthesize fake samples. Distribution-Discrepancy Condensation (DDC) adversarially optimizes these maps by matching the CFs of target fake samples and synthesized samples: a sample network learns the frequency distribution qψ(ω) that maximizes their discrepancy, while the learnable DDMs minimize it under the sampled ω . To ensure that synthesized samples remain compatible with natural images for joint training, Manifold-Consistent Replay (MCR) achieves the energy manifold requirement with a variance-preserving constraint, which naturally yields a DDPM-style composition. At task t+1 , the stored DDMs are unfolded on current real faces to generate replay fake samples that match previous forgery distributions.
Method
Privacy
Replays
Task
P1 (ACC ↑ )
P2 (AUC ↑ )
FF++
DFDCP
DFD
CDF2
AA
AF ↓
Hybrid
FR
FS
EFS
AA
AF ↓
LwF TPAMI’17 ( Li and Hoiem, 2017 )
Yes
0
T1
95.52
–
–
–
95.52
–
97.00
–
–
–
97.00
–
T2
87.83
81.57
–
–
84.70
7.69
88.76
88.45
–
–
88.61
8.24
T3
76.16
41.78
96.36
–
71.43
29.58
84.07
80.99
96.44
–
87.24
10.20
T4
67.34
67.43
84.05
87.90
76.68
18.21
78.73
56.73
93.67
92.82
79.32
17.59
DER CVPR’21 ( Yan et al., 2021 )
No
500
T1
95.45
–
–
–
95.45
–
97.00
–
–
–
97.00
–
Table 1. Main results on two widely used CFFD benchmarks. Following their original settings for fair comparison, P1 reports ACC while P2 reports AUC. “Privacy” indicates whether the method avoids directly storing raw historical face images. ⋆ denotes directly using our replay for mixed training, while † denotes combining our replay with the strong baseline SUR-LID. Best and second-best AA/AF results at each stage are highlighted in bold and underlined, respectively.
Method
Privacy
Replays
P1 (ACC ↑ )
P2 (AUC ↑ )
FF++
DFDCP
DFD
CDF2
Hybrid
FR
FS
EFS
AA
AA
AF ↓
AA
AF ↓
AA
AF ↓
AA
AA
AF ↓
AA
AF ↓
AA
AF ↓
DER ( Yan et al., 2021 )
No
500
95.45
76.21
21.59
70.59
28.87
66.03
30.21
97.00
79.38
37.97
61.92
54.45
70.50
37.56
+ Ours
Yes
50
95.42
86.95
8.93
86.69
8.69
81.87
12.62
96.37
86.86
18.53
83.01
19.24
81.08
18.93
CoReD ( Kim et al., 2021 )
No
500
95.50
90.28
2.56
87.71
7.60
81.22
11.42
96.65
86.72
3.10
84.80
4.09
81.41
9.86
+ Ours
Yes
50
95.07
90.04
2.32
89.85
4.42
85.46
6.16
96.43
86.78
2.48
84.53
4.24
83.24
7.31
Table 2. Generality of the proposed replay mechanism across replay-based continual forgery detectors. Gray rows replace the original replay module of the method above with our replay. P1 reports ACC and P2 reports AUC following the original benchmarks. Bold numbers indicate that replacing the original replay with ours improves the corresponding result.
Figure 3. Analysis of historical information encoded in replay samples under different replay budgets K∈{1,5,10,20,50} . (a) Cosine to Fake Centroid, (b) Fake-Real Margin, (c) Probe AUC, and (d) Probe AP.
Table 3. Replay-level privacy analysis. R1@1/[email protected]: ArcFace R1-Link at FAR=1%/0.1%; MIA-AUC: Replay-MIA-ArcFace AUC; LPIPS: NN-LPIPS mean; LPIPS AUC: Replay-MIA-LPIPS AUC. Lower is better except for LPIPS.
Variant
FF++
DFDCP
DFD
CDF2
AA ↑
AF ↓
AA ↑
AF ↓
AA ↑
AF ↓
AA ↑
AF ↓
Full
96.01
-
92.47
2.25
92.16
4.08
91.08
4.34
w/o DDC
96.05
-
86.32
14.50
83.24
16.31
79.82
18.36
w/o DDM Std.
96.05
-
92.35
3.25
90.08
6.54
86.50
9.86
w/o MCR
96.00
-
88.70
9.75
87.38
10.12
83.47
13.68
Table 4. Ablation study on Protocol 1. We report the average accuracy (AA) and average forgetting (AF) after each task to analyze the contribution of DDC, DDM standardization, and MCR. Best results are in bold, and the second are underlined.
Figure 4. Visualization of replay samples and distribution consistency after task-0 training on FF++. Top: representative replay samples from selection-based replay, perturbation-based replay, and our DDM replay. Bottom: visualization of characteristic functions, where angle denotes phase and radial magnitude denotes amplitude.