Title: DevFD : Developmental Face Forgery Detection by Learning Shared and Orthogonal LoRA Subspaces
Venue: NeurIPS 2025
Accepted: yes
Keywords: Face Forgery Detection

Face Forensics

Continual Learning
Abstract: The rise of realistic digital face generation and manipulation poses significant social risks. The primary challenge lies in the rapid and diverse evolution of generation techniques, which often outstrip the detection capabilities of existing models. To defend against the ever-evolving new types of forgery, we need to enable our model to quickly adapt to new domains with limited computation and data while avoiding forgetting previously learned forgery types. In this work, we posit that genuine facial samples are abundant and relatively stable in acquisition methods, while forgery faces continuously evolve with the iteration of manipulation techniques. Given the practical infeasibility of exhaustively collecting all forgery variants, we frame face forgery detection as a continual learning problem and allow the model to develop as new forgery types emerge. Specifically, we employ a Developmental Mixture of Experts (MoE) architecture that uses LoRA models as its individual experts. These experts are organized into two groups: a Real-LoRA to learn and refine knowledge of real faces, and multiple Fake-LoRAs to capture incremental information from different forgery types. To prevent catastrophic forgetting, we ensure that the learning direction of Fake-LoRAs is orthogonal to the established subspace. Moreover, we integrate orthogonal gradients into the orthogonal loss of Fake-LoRAs, preventing gradient interference throughout the training process of each task. Experimental results under both the datasets and manipulation types incremental protocols demonstrate the effectiveness of our method.

DevFD: Developmental Face Forgery Detection by Learning Shared and Orthogonal LoRA Subspaces

Abstract

The rise of realistic digital face generation and manipulation poses significant social risks. The primary challenge lies in the rapid and diverse evolution of generation techniques, which often outstrip the detection capabilities of existing models. To defend against the ever-evolving new types of forgery, we need to enable our model to quickly adapt to new domains with limited computation and data while avoiding forgetting previously learned forgery types. In this work, we posit that genuine facial samples are abundant and relatively stable in acquisition methods, while forgery faces continuously evolve with the iteration of manipulation techniques. Given the practical infeasibility of exhaustively collecting all forgery variants, we frame face forgery detection as a continual learning problem and allow the model to develop as new forgery types emerge. Specifically, we employ a Developmental Mixture of Experts (MoE) architecture that uses LoRA models as its individual experts. These experts are organized into two groups: a Real-LoRA to learn and refine knowledge of real faces, and multiple Fake-LoRAs to capture incremental information from different forgery types. To prevent catastrophic forgetting, we ensure that the learning direction of Fake-LoRAs is orthogonal to the established subspace. Moreover, we integrate orthogonal gradients into the orthogonal loss of Fake-LoRAs, preventing gradient interference throughout the training process of each task. Experimental results under both the datasets and manipulation types incremental protocols demonstrate the effectiveness of our method.

1 Introduction

The swift evolution of generative models, including Generative Adversarial Networks (GANs) [2, 38, 62] and Diffusion models [17, 18, 61], along with the rise of large models [4, 25, 50, 63, 71], propels the rapid advancement of face forgery technology, which introduces significant security risks and contributes to a crisis of public trust. These manipulation methods leave unique traces of forgery. Existing detectors perform well with known forgery types but struggle to identify novel ones. When new manipulation types arise, a trivial method is to add new forgery data to the primary dataset and retrain a binary model. However, the rapid evolution of forgery techniques necessitates a new learning strategy that can quickly adapt to new forgeries using limited computation and data.

Real

Faces

Developmental MoE

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Type1

Fake

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Fake

TypeN

Shared LoRA

Orthogonal LoRAs

Real Samples

Evolving Forgery Samples

Real Samples

Evolving Forgery Samples

(a) t-SNE visualization on FF++ and CDF2.

(b) Developmental MoE Forgery Detection Framework

FF++ _Real

Deepfakes

Face2Face

FaceSwap

NeuralTextures

CDF2_Fake

CDF2_Real

Label-guided Localized Balancing

Classifier

or

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Backbone

Rapidly Evolving Forgery Types

In contrast to the rapidly evolving fake faces, real faces possess distinct characteristics. Genuine facial data is relatively abundant, stable, and acquired through a single modality, such as camera imaging. In the context of forgery detection, it typically does not introduce bias. These properties, which remain invariant as forgery techniques evolve, endow real faces with commonalities that may easily be overlooked. Fig. 1(a) presents a simple t-SNE [57] visualization experiment, using the baseline [8] model to extract features from both the FF++ [48] and CDF2 [27] datasets. The results reveal two key observations: (1) Real face samples from both datasets exhibit close distributions, while (2) forged samples form five distinct clusters. These observations inspire us to enable the model to adapt to emerging manipulation methods while learning the commonalities of real faces.

In this paper, we approach face forgery detection as a continual learning problem and make the model "developmental," enabling it to adapt to new forgery types continuously. As shown in Fig. 1(b), we propose a developmental Mixture of Experts (MoE) architecture, DevFD, which employs Low-Rank Adaptation (LoRA) models as individual experts to fine-tune pre-trained models within different subspaces. As illustrated in Fig. 2, the model expands a new LoRA branch when adapting to an unknown forgery type. However, adapting to new forgery types can disrupt the knowledge in established subspaces, leading to catastrophic forgetting. To address this, we use an orthogonal loss to constrain the learning direction of new LoRAs to be orthogonal to all previously established LoRAs, thereby preserving the learned knowledge. This part of the LoRAs ultimately forms an orthogonal sequence termed Fake-LoRAs. For real faces that are stable and exhibit common properties, modeling them with the same sequence overlooks their overall distribution. Therefore, we introduce a separate shared expert, termed Real-LoRA, to fine-tune and refine knowledge about real faces. To enable the Real-LoRA and Fake-LoRAs to align with their assigned roles and avoid disrupting the orthogonality of Fake-LoRAs, we design a label-guided localized balancing strategy that softly constrains the experts through a weighted response matrix. For unknown forgery images, the router aggregates the outputs of all LoRAs to make a joint decision, thereby maintaining collaboration among the experts.

Additionally, we theoretically analyze that the subspace orthogonal loss still causes forgetting due to the early stages of optimization not satisfying the orthogonality condition, where learning new tasks disrupts the established knowledge of other subspaces. To avoid this, we integrate orthogonal gradients into the orthogonal loss and propose a new integrated orthogonal loss to constrain the FakeLoRAs. Experiments demonstrate that our method achieves state-of-the-art average accuracy and the lowest average forgetting rate in continual learning experiments on both the datasets incremental protocol and the manipulation types incremental protocol.

Contributions. We summarize our contributions as follows:

We propose a developmental MoE architecture to address the continuous emergence of new forgery types. We use a label-guided localized balancing strategy to allocate Real-LoRA to model the real face information, while Fake-LoRAs capture the incremental fake face information.

Based on theoretical analysis, we integrate orthogonal gradients into the subspace orthogonal loss and form a new integrated orthogonal loss to mitigate the interference of gradients on the knowledge of established subspaces during the entire stages of training.

Extensive experiments demonstrate that our proposed method achieves the highest average scores and the lowest average forgetting rate among all comparison methods.

2 Related Works

2.1 Face Forgery Detection

Face forgery detection requires the model to classify input images or videos as either real or fake. Related methods [5, 6, 10, 33, 58, 67] focus on identifying forgery cues present in the forged media and can be categorized into two major groups. The first category designs models that incorporate additional information to aid in detection, such as frequency analysis [13, 24, 35, 42], wavelet [36], graphics [72, 73], language descriptions [54], and audio-video consistency [39]. The second category [23, 26, 51] is data-driven, utilizing only real faces and training models through a self-supervised approach. With the continuous evolution of forgery methods, continual learning is applied to forgery detection. DFIL [40] and HDP [55] treat the learning of forgery datasets as a sequence of sub-tasks, requiring the model to learn sequentially across these tasks, achieving satisfactory performance. In this work, we design a Developmental MoE to address the challenges of continual learning in face forgery detection.

2.2 Continual Learning

Continual learning typically models the task as a sequence of sub-tasks, requiring the model to adapt to dynamic data distributions incrementally. The goal is to achieve high performance on newly learned tasks while avoiding catastrophic forgetting of information from previous tasks. Related approaches can be broadly divided into three categories: Regularization-based Approaches [47, 49, 53, 59, 65, 70], Replay-based Approaches [22, 34, 45, 46, 52], and Optimization-based Approaches [11, 21, 31, 32, 41]. Among the latest methods, OrCo [1] enhances generalization in class-incremental learning through orthogonality and contrast. Meanwhile, MoECL [68] adapts to new tasks by introducing an MoE. DFIL [40] and SUR-LID [7] achieve satisfactory results using replay mechanisms. In this work, we treat the iteration of fake faces as an incremental learning problem and employ a developmental architecture to facilitate continual learning.

2.3 Mixture of Experts

The Mixture of Experts (MoE) is a set of sparse models with multiple experts and a routing network. By training MoE in parallel, methods [14, 37, 74] enable efficient fine-tuning of large models with minimal computational cost. MoECL [68] introduces MoE to continual learning, employing an intuitive trainable-freezing strategy to reduce the model's forgetting rate. Low-rank Adaptation (LoRA) [15] further decreases parameter and computational cost by mapping inputs into a lowrank subspace. O-LoRA [60] utilizes LoRAs as experts and constrains the orthogonality of LoRA subspaces. InfLoRA [30] further applies orthogonal LoRA to vision tasks, improving the performance of multiple sub-tasks through pre-defined orthogonal subspaces. MoEFFD [20] integrates MoE and LoRA into face forgery detection, allowing each expert to specialize in extracting features related to different forgery techniques. We use the LoRA module to construct an MoE architecture to address continual face forgery detection.

3 Methodology

We cast forgery detection as a continual learning problem D = {D 1 , . . . , D T } , where T is the total number of tasks. For any task D t , let x i,t be the sample and y i,t its corresponding label, and n t represent the number of samples in the current task. The model sequentially learns each task

or

Input Faces

Layer Norm

Classifier

Label-guided Localized Balancing

Multi-Head

Self Attention

Feed Forward

(FFN)

Devlopmental

MoE

x N

...

Devlopmental MoE

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Gradient Space

Current LoRA

Subspace

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Fake

D t = { ( x i,t , y i,t ) } n t i =1 = {X t , Y t } in the task sequence D to adapt to new types of forgery. Based on the characteristics of the face forgery detection task, we provide the following descriptions:

̸

̸

̸

where p ( · ) is the sample distribution, X is the samples and Y is the labels. For fake faces, different forgery types belong to different domains but share the same label Y = "Fake", presenting a domain incremental problem. In contrast, due to the uniform acquisition methods and the stable and abundant samples of real faces, real faces across datasets exhibit similarity. We aim to model and align them into a unified distribution, i.e., p ( X i ) = p ( X j ) . We strictly isolate training data across tasks.

3.1 Developmental MoE Architecture

We propose a novel developmental MoE architecture using LoRAs as individual experts to fine-tune pre-trained models within different subspaces; the overview is shown in Fig. 2. First, we employ a Vision Transformer (ViT) pre-trained on real faces as the backbone, denoted as g Φ ( f Θ ( · )) , where f Θ represents the pre-trained ViT network, and g Φ denotes the classifier. We freeze the parameter Θ of the ViT backbone f and introduce LoRA to fine-tune the Feed Forward Network (FFN) weights in each transformer block. For a parameter matrix of any FFN layer W ∈ R d O × d I with input dimension d I and output dimension d O , LoRA decomposes the parameter change matrix ∆ W into the multiplication of two low-rank matrices:

where B ∈ R d I × r is the dimensionality reduction matrix and A ∈ R r × d O is the dimensionality expansion matrix. Generally, the ranks r of the two matrices are equal and satisfy the condition: r ≪ min( d O , d I ) . To enable the model to adapt to each task, as illustrated in Fig. 2, we design a developmental LoRA sequence as MoE adapters:

Here, x is the input, while e is the output. The LoRA modules in the sequence { A j B j } t j =0 are assigned by a label-guided localized balancing strategy (LLB) for two purposes: the RealLoRA A 0 B 0 is designed to learn and refine the common information of real faces modeled by the backbone, while the Fake-LoRAs { A j B j } t j =1 compose an orthogonal sequence for learning fake face information specific to each task.

3.2 Learning LoRA Subspaces enhanced by Orthogonal Gradients

The reason for catastrophic forgetting is that parameter adjustments while learning the current task disrupt the knowledge acquired from previous tasks [30]. The subspace established by LoRA can approximately represent the information acquired for a new task [60]. As a developmental structure, DevFD constrains the learning direction of the expanded branches for the new task to prevent interference with previously established subspaces by using an integrated orthogonal loss enhanced with orthogonal gradients. First, the learning of the t-th task can be modeled as follows:

where W is the parameter matrix of the FFN layer, e and x represent the output and input of the network, respectively. Existing methods attempt to use orthogonal loss to control the learning direction of LoRA to be orthogonal to the established subspaces in order to prevent forgetting. However, the forgetting remains severe. We demonstrate that relying solely on the subspace orthogonal loss is insufficient and present an orthogonal gradient solution to further mitigate forgetting.

Why Orthogonal LoRA Subspaces are Still Forgetful? The adaptation of LoRA to new tasks involves fine-tuning the weight matrices within their respective subspaces. LoRA maps the input to the subspace : span { b t 1 , . . . , b t r } , where the row vectors { b t 1 , . . . , b t r } of the dimensionality reduction matrix B t serve as a basis for this subspace. O-LoRA [60] enforces the orthogonality between the basis { b t 1 , . . . , b t r } and the basis { b i 1 , . . . , b i r } of the forward LoRAs' subspace, ensuring that the subspaces of the LoRA sequence are mutually orthogonal. Let O i , t be the dot product of two bases of these subspaces. The orthogonality optimization objective can then be expressed as:

Notably, the orthogonality condition exists in the form of an optimization objective. We will demonstrate that preventing the disruption of the information in the previous subspaces depends on the strict B T t B i = 0 throughout the training process. We begin with a proven proposition [30]: when the model learns the t-th task, fine-tuning A t is equivalent to fine-tuning the original parameter matrix W within the subspace span { b t 1 , . . . , b t r } , denoted as:

where ∆ A t W t is the increment of the composed matrix W t caused by the change of A t , and ∆ W W t is the increment of the composed matrix W t caused by the change of the original parameter W . Thus, a projection matrix B T t B t projects each row vector of ∆ W W t into the subspace span { b t 1 , . . . , b t r } , it follows that the increment ∆ A t W t due to the fine-tuning of A t is equivalent to mapping ∆ W W t into span { b t 1 , . . . , b t r } . In summary, the increment ∆ A t W t will not affect the subspace established by previous LoRA, under the assumption that span { b t 1 , . . . , b t r } is orthogonal to these subspaces, meaning that the orthogonality condition B T t B i = 0 needs to hold strictly during the entire training process. Therefore, we argue that catastrophic forgetting occurs in the early stages of training on new tasks when the bases B t and B i are not perfectly orthogonal. The gradients of the new tasks can disrupt the established subspaces.

Integrate Orthogonal Gradients into Orthogonal Loss. To address the issues discussed above, we choose to directly constrain the gradient space while optimizing the orthogonality of subspaces, resulting in a new integrated orthogonal loss. First, it is proven that the gradient update of a linear or convolution layer lies within the span of the input vectors [29]. We place the proofs in the Appendix B. This proposition allows us to transform the constraints on the gradient space into one on the input space. Let the input matrix be H ∈ R d I × n , where n is the batch size. Due to the inconsistency in matrix dimensions, we first perform Singular Value Decomposition (SVD) on H T t :

We then choose the rows of V T t corresponding to the top-r singular values and compose ( V T t ) r ∈ R r × d I . We use ( V T t ) r as an estimate of the gradient space and constrain it to be orthogonal to the previously established subspace. Let G i , t represent the dot product between the estimated gradient space and the previous subspace basis. The gradient constraint can then be expressed as:

In summary, we integrate the orthogonality of LoRA's subspace and the gradient space, designing the integrated orthogonal loss as follows:

Here, ∑ ∥·∥ 2 represents the sum of the squares of the matrix elements, λ 1 and λ 2 are hyperparameters. L ort constrains the subspace and the gradient space of the current LoRA to be orthogonal to those of the previous LoRAs, thereby alleviating forgetting.

3.3 Label-guided Localized Balancing Strategy

To model the commonalities of real faces and continuously refine the real face knowledge represented by the backbone in a continual learning sequence, we establish an additional LoRA Expert A 0 B 0 , referred to as Real-LoRA, and make it learnable across all tasks. However, the Real-LoRA introduces two issues. First, the additional Real-LoRA disrupts the orthogonality of the LoRA sequence, leading to interference with the Fake-LoRA sequence. Second, it cannot be guaranteed that the Real-LoRA will learn real-face information. To address these issues, we propose a label-guided localized balancing strategy. This strategy dynamically adjusts the responses of experts to different types of samples based on the labels during training. Specifically, it enables Real-LoRA to focus more on real faces, while Fake-LoRAs concentrate on learning forged faces in an orthogonal manner, preventing Real-LoRA from disrupting the orthogonality of Fake-LoRAs. The proposed strategy first takes the inputs from all LoRAs and sets a response matrix I ∈ R ( t +1) × n , where t +1 indicates the total number of experts. An element I k,l indicates the response of the k -th expert to the l -th sample in the batch. It is defined as follows:

Here, ω j k,l represents the output of the k -th expert for the j -th token in the l -th sample, T m denotes the number of tokens in the l -th sample. All outputs of LoRA can be defined as a vector matrix O ∈ R ( t +1) × n × d o , where each element is defined as the output of the k -th expert for the l -th sample. Based on the response matrix I and the output matrix O , the forward process computes the final output e ∈ R n × d o , where the output vector e l ∈ R d o for the l -th sample is defined as:

To allocate Real-LoRA and Fake-LoRAs for modeling the information of real and fake faces, respectively, we utilize labels to define a balancing coefficient matrix C ∈ R ( t +1) × n . An element C k,l represents the balancing weight applied to the response at the corresponding position I k,l :

̸

where Type e ( k ) = Type h ( l ) indicates that the expert matches the category label corresponding to the sample, such as Real-LoRA for real faces, the coefficient matrix C decreases the balancing coefficient for this item; otherwise, C increases the balancing coefficient. The increment δ of the coefficient falls within the range of [0 , 1] . Based on the response matrix I and the balancing coefficient matrix C , the LLB loss constrains the dispersion of the weighted response matrix I ◦ C :

where σ 2 ( · ) and µ ( · ) represent the variance and mean, respectively, and ◦ is element-wise multiplication. This loss function forces the weighted responses ( I ◦ C ) to become as balanced as possible. By optimizing this loss function, the model is thus compelled to generate a greater original response I k,l for the matching pairs (to compensate for their small 1 -δ balancing coefficients) and a smaller original response for the mismatched pairs. Additionally, this router does not select specific LoRAs but encourages collaboration among all LoRAs.

When the model begins to learn a new task, DevFD establishes a new LoRA branch and freeze the other Fake-LoRAs in the orthogonal sequence { A j B j } t j =1 , allowing only the newly added LoRA A t B t and the Real-LoRA A 0 B 0 to be trainable. During inference, it aggregates the outputs of all LoRA experts to make a joint decision, fostering collaboration among them.

In summary, we define the total loss as the weighted sum of the binary cross-entropy loss L cls, the orthogonal loss L ort, and the label-guided localized balancing loss L llb :

where λ 3 is a hyperparameter. This loss allows real faces and fake faces from different forgery manipulation types to be modeled by distinct LoRAs without mutual interference and encourages collaboration among all LoRAs in this MoE architecture.

4 Experiments

4.1 Experimental Setup

Datasets. To facilitate comparisons, we select multiple datasets, employing different forgery methods to manipulate and generate faces from distinct domains. FaceForensics++ (FF++) [48] contains 1K real face videos. These faces are manipulated by four forgery methods, resulting in a total of 4K fake videos. The Deepfake Detection (DFD) [12] dataset comprises over a hundred real face samples and more than 1K forged face samples. For the Deepfake Detection Challenge (DFDC) [9] dataset, we utilized the Preview version (DFDC-P), which includes 5K videos and two forgery methods. Celeb-DF v2 (CDF2) [27] comprises 590 real and 5,639 fake videos. DF40 [66] is a large-scale dataset comprising 40 types of forgery methods and over 100K fake videos. We select [FF++, DFDC-P, DFD, CDF2] as our dataset-incremental testing protocol. From DF40, we choose three forgery methods corresponding to different forgery types: Face-Swapping (FS): BlendFace, Face-Reenactment (FR): MCNet, and Entire Face Synthesis (EFS): StyleGAN3. The datasets of selected forgery methods and a base dataset with hybrid forgery types FF++ jointly compose our forgery-type-incremental testing protocol: [Hybrid, FR, FS, EFS].

Implementation Details and Metrics. We utilize a pre-trained ViT model as the initial backbone. The pre-training data is sourced from real faces in the FF++ dataset. We conduct self-supervised pre-training through a data augmentation approach [51]. Regarding data utilization, we strictly isolate data across tasks, including real and fake faces. Similar to DFIL [40], we randomly sample 100 videos from the current dataset and train for 20 epochs in each task, maintaining a balance between real and fake samples. The hyperparameter settings are adjusted according to the number of training epochs, and detailed information is provided in Appendix C.2. We employed the Adam optimizer with parameters set to β 1 = 0 . 9 , β 2 = 0 . 999 . The learning rate is set to 1 e -4 , and the batch size is set to 128. The rank (subspace dimension) for LoRA was determined using a grid optimization algorithm similar to InfLoRA [30]. We utilize Accuracy (Acc) and Area Under the Curve (AUC) to evaluate the overall performance of the model. Average Forgetting (AF) indicates the model's ability to retain information from previously learned tasks. Let a t,i denote the evaluation Score (Acc or AUC) on task i after the model has been trained up to task t . The AF T (for T > 1 ) is defined as:

where a T,i is the score on task i after learning the final task T .

4.2 Comparison Experiments

Experiments on Dataset Incremental Protocol. To comprehensively evaluate the performance of the proposed method, we conduct comparative experiments with general continual learning

approaches (e.g., LwF [28], ER [3], and SI [69]) and specialized continual face forgery detection methods (e.g., CoReD [19], DFIL [40], and DMP [56]). We ensure that the improvements stem from the new strategy and maintain the fairness of the experiments, as detailed in Appendix C.1. Under the dataset incremental protocol, the model is sequentially trained on task sequences constructed from [FF++, DFDC-P, DFD, CDF2]. After completing each task's training phase, we evaluate model accuracy on both the current and previously learned tasks. Quantitative results are organized in a lower triangular accuracy matrix, accompanied by two key metrics: average accuracy (Avg) and average forgetting (AF), as detailed in Table 1-Left. The results reveal that general continual learning methods initially achieve satisfactory accuracy but suffer severe performance degradation on previous tasks as training progresses, resulting in higher forgetting rates. In contrast, continual forgery detection approaches exhibit more stable performance but still suffer from forgetting. It is worth noting that many of the compared methods [7, 40] utilize a replay set, while our method strictly isolates all subtask data and still achieves superior anti-forgetting and state-of-the-art performance.

Experiments on Manipulation Types Incremental Protocol. The dataset incremental protocol tested above demonstrates our method's superior performance and anti-forgetting capability. Considering potential issues in the dataset incremental protocol, such as the domain gaps between different datasets and the possibility of shared manipulation methods across datasets, we perform experiments under the manipulation types incremental protocol [Hybrid, FR, FS, EFS], employing AUC as the primary metric for comparison. The experimental results are presented in Table 1-Right. The results show general continual learning approaches (LwF [28], iCaRL [44], DER [64]) exhibit unstable performance with high forgetting rates. Even specialized forgery detection methods demonstrate initial training instability - DFIL [40] shows a 40.72% performance drop on previous tasks during FR-type training. Our method maintains stable learning throughout the entire training sequence, achieving both the highest final performance and the lowest forgetting rate. Comparative analysis reveals significant advantages: our approach surpasses SUR-LID [7] by 0.57% in average AUC, with

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0.55% lower forgetting rate, and outperforms HDP [55] by 16.64% AUC margin while reducing forgetting rate by 20.21%. These results validate our method's performance against manipulation-type shifts in continual learning scenarios.

4.3 Long-sequence Continual Learning Experiments

To further validate the effectiveness of our proposed method on more manipulation types and extended task sequences, we selected 10 datasets from DF40 and completed long-sequence continual learning experiments under the Task10 protocol. We provide more details in the Appendix C.3. The average AUC metric and forgetting rate are plotted as shown in Fig. 3. The experimental results indicate that in long-sequence learning, both DFIL [40] and HDP [55] exhibit unstable catastrophic forgetting. At the same time, our method achieves the highest average AUC and the lowest forgetting rates.

4.4 Motivation Validation Experiments

Our proposed method is based on a key observation: abundant and unbiased real faces exhibit a more compact distribution across datasets than forged faces created by various methods. Building upon a t-SNE experiment that validates this observation, we further confirm that real faces occupy a more similar input space than forged faces within our proposed framework. We first theoretically demonstrate that a more similar input space corresponds to a smaller orthogonal gradient loss. Specifically, when a new orthogonal LoRA is configured to model a highly similar domain, this domain will possess a highly similar input space H t ∈ R d I × n . From Eqn. 7, it can be deduced that the resulting V T t will also be highly similar. Consequently, in Eqn. 8 and Eqn. 9, the term ∑ ∥ G i,t ∥ 2 yields a near-zero value because its components are approximately orthogonal.

Based on this, we design a new experiment consisting of two separate orthogonal LoRA sequences: one for modeling real faces and the other for modeling forged faces. These sequences learn from the task series [FF++, DFDCP, DFD, CDF2]. We record the range of orthogonal gradient loss values for the two sequences during the training process to investigate the cross-dataset similarity of their respective input spaces. The experimental results are presented in Fig. 4. Starting from the second task, the orthogonal gradient induced by the real face data for a new LoRA is smaller than that induced by the forged face data. The results suggest that real faces from different datasets share a

similar input space H t , leading to an orthogonal gradient loss that is an order of magnitude smaller. Moreover, the orthogonal LoRA sequence introduces additional computational overhead compared to a single Real-LoRA. This experiment mutually validates our initial t-SNE results, demonstrating that using a single, shared Real-LoRA to model real faces is both reasonable and necessary.

4.5 Ablation Study

Effect of L ort and L llb . We employ orthogonal loss L ort to constrain both the subspace and gradient space of the currently learned LoRA and use L llb to allocate the Real-LoRA and Fake-LoRAs. We conduct ablation studies on both losses, with the experimental results presented in Table 2. L olora denotes the use of orthogonal subspaces without orthogonal gradients to validate the effectiveness of our orthogonal gradients; 'All" represents the model's performance incorporating all three losses. The results reveal that the L ort significantly enhance the model's anti-forgetting capability. Meanwhile, L llb primarily improves the model's Average Accuracy (AA) by facilitating task-specific allocations among LoRAs and maintaining a weighted response matrix for collaboration.

Effect of Real-LoRA and Fake-LoRAs. To study the effectiveness of the designed LoRA modules, we separately isolate the two types of LoRA and report the results in Table 3. We differentiate our models based on the number of different types of LoRAs used. Specifically, '4' indicates the use of an orthogonal LoRA sequence (with a sequence length of 4 tasks), '1' indicates the use of a single global LoRA, and '-' indicates that no LoRA module is used. The L llb is introduced only when both types of LoRAs are used simultaneously. To investigate whether the global Real-LoRA would lead to catastrophic forgetting of real faces, we add an additional experiment shown in the last row by expanding the shared Real-LoRA into a sequence identical to that of Fake-LoRAs, i.e., using two orthogonal LoRA sequences to learn real and fake samples separately. The experimental results show that Fake-LoRAs have a significant effect on mitigating catastrophic forgetting. After incorporating both types of LoRA, the addition of Real-LoRA improves both accuracy and the forgetting rate. Moreover, replacing Real-LoRA with an orthogonal sequence does not bring significant improvements in forgetting rate or accuracy. Therefore, the Real-LoRA does not cause catastrophic forgetting.

5 Conclusion

In this paper, we propose DevFD, a Developmental MoE architecture for continual face forgery detection. By utilizing LoRA models as experts, DevFD expands with new LoRA branches to adapt to emerging types of forgery. We employ a label-guided localized balancing strategy to allocate all experts for two purposes: the Real-LoRA refines the real face knowledge modeled by the backbone continuously, while the Fake-LoRAs capture incremental forgery cues from different types. To prevent catastrophic forgetting, we use an orthogonality loss to constrain the learning direction of the learning LoRA to be orthogonal to the existing subspaces. Additionally, we integrate orthogonal gradients into the subspace orthogonal loss to mitigate the interference of gradients on the established subspace knowledge during the entire training phase. Experimental results demonstrate that our approach achieves the best performance.

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Appendix

A Limitations

First, the first limitation lies in the linear increase of our method's model scale as the training sequence progresses. Although the number of parameters in LoRA is very small, this growth may have an impact on extremely long sequence continual learning. Second, our method fine-tunes the pre-trained weights within subspaces using LoRA, and thus may rely on a strong general pre-trained model that already possesses some general knowledge before training. We will address these limitations in our future research.

B Details of Orthogonal Gradient

To estimate the gradient space of LoRA and orthogonalize it with the subspace of LoRA, we rely on a key proposition [29] that the gradients of linear layers will lie within the input space. Here, we provide proof of this proposition. For a linear layer, let its weight matrix be denoted as W ∈ R d I × d O . Its forward process can be expressed as:

where x ∈ R d I and e ∈ R d O are the input and output vector, respectively. By applying the chain rule, we compute the gradient of W :

Assuming the vector ∂ L ∂ e is given by: [ l 1 , l 2 , ..., l d O ] T , then the gradient ∂ L ∂ e x T can be represented as:

It can be observed that each row of the gradient ∂ L ∂ W is a multiplication of the input vector x with a certain value l k ( 1 ≤ k ≤ d O ). Hence, the gradient shares the same subspace as the input vector x , meaning the gradient lies within the input space.

C More Experimental Details

C.1 Experimental Fairness

First, at the data level, we use the same amount of subtask data as the compared methods, i.e., 100 videos with 20 sampled frames per video, which is identical to methods such as DFIL [40] and SUR-LID [7]. Second, at the model level, we employ a backbone with a similar order of magnitude of parameters as the compared methods: ViT-B/16, thus avoiding the use of a larger model to ensure experimental fairness. It is worth noting that our trainable parameters consist only of the parameters of two LoRAs, which is approximately 1/10 of the trainable parameters of the compared methods. Third, in terms of training strategies, many methods such as DFIL [40] and SUR-LID [7] utilize a Replay Set, which allows access to data from previous tasks-a practice that is prohibited in many continual learning scenarios. The proposed DevFD method completely isolates any data between subtasks. This not only demonstrates that we achieve the best results under stricter constraints but also indicates the broader applicability of our method. Finally, the comparative metric data used are the best results extracted directly from the original papers. We achieve an undeniably leading position in absolute performance.

C.2 More Implementation Details

We provide our detailed hyperparameter settings here. For the label-guided localized balancing strategy, we use a fixed hyperparameter setting. We set δ to 0.15 and λ 3 to 0.2. For the integrated

20x

orthogonality loss, since our goal is to prevent the gradients in the current LoRA from interfering with the subspaces established by previous tasks during the entire training phase, we dynamically adjust the hyperparameters of the orthogonal subspace loss to the orthogonal gradient loss for each epoch. Specifically, from epoch [0,5), we set λ 1 to 0.5 and λ 2 to 0.5. From epoch [5,10), we set λ 1 to 1 and λ 2 to 0.1. From epoch [10,20), we set λ 1 to 1 and λ 2 to 0.01.

C.3 More Details of Task10 Long Sequence Experiment

Existing continual learning methods for forgery detection typically employ task sequences with only four subtasks. We provide results for a continual learning task sequence with 10 subtasks based on DF40 [66] and reproduce three high-performing continual learning methods for comparison: CoReD [19], DFIL [40], and HDP [55]. We start with the original task sequence [Hybrid, FR, FS, EFS], where Face-Swapping (FS): BlendFace, Face-Reenactment (FR): MCNet, and Entire Face Synthesis (EFS): StyleGAN3 are used as our initial learning sequence and expand it to 10 tasks. We carefully select the remaining six methods while maintaining diversity. Specifically, for FaceSwapping (FS), we choose SimSwap and InSwapper; for Face-Reenactment (FR), we select Wav2Lip and SadTalker; and for Entire Face Synthesis (EFS), we choose StyleGAN2 and StyleGAN-XL. We then randomly shuffle the last six methods to form the complete task sequence with 10 subtasks: [FFpp, MCNet, BlendFace, StyleGAN3, StyleGAN-XL, Wav2Lip, SadTalker, StyleGAN2, SimSwap, InSwapper]. Analysis reveals that EFS methods may have significant differences from FS and FR methods, leading to sharp increases in forgetting rates during transitions between learning EFS methods and FS & FR methods, such as 5->6 and 7->8.

D More Experimental Results

D.1 Parameter Analysis

We introduce a Developmental Mixture of Experts: DevFD to tackle the challenge of detecting new types of face forgeries. As new tasks are integrated, the parameters of the DevFD model expand in a specific manner. To illustrate that our developmental MoE can handle long-sequence tasks without suffering from parameter explosion, we perform experiments to track the expansion in parameter volume, as depicted in Fig. 5. By employing Parameter-Efficient Fine-Tuning (PEFT) technology, our DevFD model can learn in long-sequence tasks with a minimal increase in parameter volume. The experimental results show that when the model is fine-tuned for the first task in the sequence, the trainable parameters constitute only 1.18% of the total parameters. Even after training on all four tasks, the additional parameters account for just 2.89% of the total. Furthermore, since only the Real-LoRA and the final LoRA in the Fake-LoRA sequence are trainable, the number of trainable parameters remains stable throughout the task sequence and does not expand with the overall parameter volume. Consequently, the parameters of our proposed developmental MoE are fully manageable, making it well-suited for learning in long-sequence tasks.

AverageAccuary(AA)

AverageForgetting(AF)

90

25

LoSS

88

20

O+G

86

15

A

84

10

82

LoSS

80

O+G

5

50

10020050010002000

50

10020050010002000

D.2 Effect of orthogonal Subspace and Orthogonal Gradients

The proposed method introduces additional gradient constraints to ensure that even when the subspaces are not entirely orthogonal, the gradient space remains orthogonal to the established subspaces. This prevents the gradient from interfering with the established subspaces. We simulate the early stages of the training process using fewer training samples to evaluate whether these constraints on the gradient space can prevent interference and the results shown in Fig. 6. Here, O represents the scenario with only subspace constraints, while O+G indicates the presence of both subspace and gradient space constraints. The experimental results demonstrate that the orthogonal gradients significantly reduces the average forgetting in the early stages of training. Additionally, it can improve the average accuracy by constraining the early learning direction, confirming the effectiveness of orthogonal gradients.

D.3 Effect of ViT Backbone

We employ a Vision Transformer (ViT) pre-trained by CLIP [43] as our initial backbone for training. To explore how different backbones affect model performance, we test various ViT models [16] with varying sizes of parameters. The results of these experiments are detailed in Table 4.

Our experiments reveal that a patch size of 16x16 performs better than 32x32, with the ViT-B/16 model demonstrating superior overall performance compared to the ViT-B/32 model. When considering the effect of increasing the parameter scale, the ViT-L/14 model, which adds 218M parameters to the

ViT-B/16, only saw a modest improvement in average accuracy but a slight increase in the average forgetting rate when trained on all four tasks. We attribute this marginal gain to the limited dataset size, which makes model performance less sensitive to parameter scale increase. Based on these findings, we choose the ViT-B/16 as our backbone model for its optimal balance of performance and efficiency.

D.4 Effect of Task Orders

To determine if the high accuracy and resistance to forgetting in our proposed method are independent of the specific order in which tasks are presented, we conducted a series of experiments varying the task sequences. The outcomes of these experiments are detailed in Table 5. Our findings indicate that our method consistently delivers high detection accuracy and resistance to forgetting across different task sequences. For instance, when the tasks are sequenced as [FF++, DFDC-P, CDF2, DFD], the model achieves an average accuracy of 90.23% and an average forgetting rate of 4.15%. In another sequence, [FF++, DFD, DFDC-P, CDF2], the model's average accuracy is 90.54% with an average forgetting rate of 3.29%. These results suggest that the order of tasks does not significantly impact the model's performance.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work proposes to address the challenge of continual face forgery detection. The authors assume that the real faces are more stable and singular compared to fake ones. The proposed framework incorporates LoRA-based experts split into two groups: one for refining real face features and others for learning different forgery types incrementally. To avoid catastrophic forgetting, Fake-LoRAs are trained with orthogonal constraints on their learning directions. Experiments conducted on various datasets and forgery types show the method’s effectiveness.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- The paper is well-structured and easy to follow. \n\n- The dataset-incremental and manipulation-incremental results are promising, and the ablation studies well demonstrate the effectiveness of the designed components.\n\nWeaknesses\n- The novelty of this work appears to be limited. Adapting MoE to continual learning has been studied in [A], and the MoE learning scheme for  face forgery detection has been explored in [B]. Moreover, applying SVD for deepfake detection has been previously studied [C].\n\n- This work is based on a strong assumption that real faces are stable and can be modeled by one single Real-LoRA, which appears to be a key contribution of this work. However, this assumption is only supported by one t-SNE map of two datasets. More preliminary studies are expected to supprot this assumption. \n\n- Although the proposed method demonstrates good performance on continual face forgery detection benchmarks, the model architecture lacks design specifically tailored to the task of face forgery detection. As such, the work offers limited insight into the reasons behind the achieved performance gains. \n\n- The experiments are conducted on four datasets, including FF++ and three Deepfake datasets: DFDCP, DFD, and CDF2. Additional experiments focusing on continual learning across unseen manipulation types would better demonstrate the generalizability of the proposed method. For manipulation-incremental experiments, it is unclear why the authors only pick three manipulation types from DF40. \n\n[A] Boosting continual learning of vision-language models via mixture-of-experts adapters, CVPR, 2024.\n\n[B] Moe-ffd: Mixture of experts for generalized and parameter-efficient face forgery detection, ArXiv, 2024.\n\n[C] Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection, ICML 2025.\n\nQUESTIONS:\nSee weaknesses.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes DevFD, a Developmental Mixture of Experts (MoE) architecture designed for continual face forgery detection. Addressing the challenge of rapidly evolving forgery techniques, DevFD frames face forgery detection as a continual learning problem. The core of the method involves utilizing Low-Rank Adaptation (LoRA) models as individual experts, allocating them into two groups: a Real-LoRA to refine the understanding of genuine facial features, and a sequence of Fake-LoRAs to incrementally capture information from various, emerging forgery types. A key innovation is the integration of orthogonal gradients into the orthogonal loss for Fake-LoRAs, aiming to prevent catastrophic forgetting by ensuring that learning new forgery types does not interfere with established knowledge. Furthermore, a label-guided localized balancing strategy is introduced to appropriately allocate expert responses during training.\n\nSTRENGTHS AND WEAKNESSES:\n1. The paper introduces a well-conceived Developmental MoE architecture specifically tailored for the unique challenges of continual face forgery detection, where real faces are stable and abundant, while fake faces continuously evolve. This frames the problem effectively within a continual learning paradigm.\n2. The theoretical analysis identifying the limitations of pure subspace orthogonality for preventing forgetting, and the subsequent proposal of integrating orthogonal gradients into the orthogonal loss (L_ort), is a significant technical contribution. This mechanism directly addresses the issue of early training interference, crucial for robust continual learning.\n3 .The design to explicitly model real faces via a dedicated Real-LoRA and evolving fake faces via orthogonal Fake-LoRAs, coupled with the label-guided localized balancing strategy (L_llb), allows for efficient and targeted knowledge acquisition without mutual interference, while also fostering overall expert collaboration.\n4.DevFD demonstrates state-of-the-art average accuracy and, critically, the lowest average forgetting rates across comprehensive experiments on both dataset-incremental and manipulation-type-incremental protocols, including challenging long-sequence tasks. This provides robust evidence of its practical effectiveness. The utilization of LoRA modules as experts ensures that the model grows in a highly parameter-efficient manner as new tasks are learned, keeping the number of trainable parameters minimal compared to the backbone, which is important for scalability in long-sequence learning.\n\nWeakness:\n\n1. While the orthogonal gradient contribution is somehow novel, the fundamental concept of using LoRA with MoE for continual learning has been explored in recent works like MoECL, O-LoRA, and InfLoRA. The paper could articulate more explicitly how DevFD’s architectural design and mechanisms fundamentally differ from these existing methods, beyond just the gradient constraint, especially in how it leverages the unique asymmetry of real vs. fake data in FFD.\n\n2 .Although the paper highlights parameter efficiency, it lacks a comprehensive analysis of the computational cost (e.g., training time per task, inference time per image) compared to baseline methods. The added complexity of computing and enforcing orthogonal gradients, and the overall MoE routing, warrants a discussion on the trade-off between performance gain and computational overhead, which is critical for real-world deployment.\n\n3.The method introduces several hyperparameters (e.g.,delta,lambda3,and the dynamically adjusted lambda1, lambda2). While settings are provided, a more in-depth analysis of their sensitivity and how robustly these optimal values were determined across different tasks or initial conditions would strengthen the paper.\n\n4.While LoRA parameters are small, the long-term implications for extremely prolonged continual learning sequences (e.g., 50+ tasks) regarding total model size and memory footprint are not fully explored.\n\nQUESTIONS:\nSee the weakness.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper aims to tackle the ever-evolving new types of forgery issues. It frame face forgery detection as a continual learning problem and allow the model to scale in complexity as new forgery types emerge. Specifically,  it employs a Developmental Mixture of Experts (MoE) architecture, utilizing LoRA models as the individual experts. To prevent catastrophic forgetting, the paper ensures that the learning direction of Fake-LoRAs is orthogonal to the established subspace.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. The motivation of the paper is clear, and the proposed solution is straightforward.\n\nWeaknesses\n1. How do you define an \"expert\"? In practice, it is often difficult to accurately determine the specific attack method for an attack image. Would such hybrid or ambiguous attack types affect the performance of the current MoE (Mixture of Experts) framework?\n2. Since real data can have a wide distribution and may include new scenarios (e.g., images from different cameras, people of different ethnicities, etc.), would introducing an additional adapter specifically for real data further improve performance?\n3. The paper lacks an in-depth analysis of the routing distribution. Is there a difference in routing behavior for different types of attacks?\n4. Why does the proposed method outperform previous approaches even on a first dataset (for example, achieving 98.41% on FF++)?\n5. The method itself is essentially an extension using multiple LoRA modules, which may not be particularly novel.\n\nQUESTIONS:\nThe authors should clarify whether extending LoRA modules specifically for the real data branch could further enhance performance, and provide a more thorough analysis of the current routing distribution.\n\nLIMITATIONS:\nThe authors should clearly state the limitations of their method, such as which types of attacks it can address and which types it cannot.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-04"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis submission tries to integrate continual learning into the detection of generated faces. It uses a mixture of expert model architecture with some modifications, i.e., a real LoRA and a set of orthogonal LoRAs for fake faces. Besides, orthogonal gradient loss was formulated to alleviate inference. Experimental results showed the proposed method had effecive detection scores and low forgetting rates.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n\n1) Continual learning was integrated into the synthetic face detection task, and was showed effective to alleviate forgetting encountered with unseen types of fake faces.\n\n2) LoRAs were split into real LoRA and a set of fake LoRAs, where fake ones were designed to be orthogonal to each other to capture incremental fake face information. Optimization strategies were designed to improve its training. \n\nWeakness:\n\n1) The proposed method should be compared with more state-of-the-art deepfake video detectors, e.g. [1].\n\n2) MoE has been used in existing deepfake image detection, e.g., [2,3], and the orthogonal gradient optimization has been used in [4] for deepfake audio detection. These works should be discussed regarding the major differences. \n\n\nReferences.\n[1] Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning, https://arxiv.org/abs/2408.17065.\n\n[2] Liu, Zihan, Hanyi Wang, Yaoyu Kang, and Shilin Wang. \"Mixture of low-rank experts for transferable ai-generated image detection.\" arXiv preprint arXiv:2404.04883 (2024).\n\n[3] Cao, Huangsen, Yongwei Wang, Yinfeng Liu, Sixian Zheng, Kangtao Lv, Zhimeng Zhang, Bo Zhang, Xin Ding, and Fei Wu. \"HyperDet: Generalizable Detection of Synthesized Images by Generating and Merging A Mixture of Hyper LoRAs.\" arXiv preprint arXiv:2410.06044 (2024).\n\n[4] Chen, Yujie, Jiangyan Yi, Cunhang Fan, Jianhua Tao, Yong Ren, Siding Zeng, Chu Yuan Zhang et al. \"Region-based optimization in continual learning for audio deepfake detection.\" In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 22, pp. 23651-23659. 2025.\n\nQUESTIONS:\nPlease see Weakness.\n\nLIMITATIONS:\nNA.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-06"}