Generalized and Parameter-Efficient Face Forgery Detection using Mixture of Experts

Core Concepts

This work introduces Mixture-of-Experts modules for Face Forgery Detection (MoE-FFD), a generalized yet parameter-efficient vision transformer-based approach that only updates lightweight Low-Rank Adaptation (LoRA) and Adapter layers while keeping the ViT backbone frozen, thereby achieving parameter-efficient training. MoE-FFD leverages the expressivity of transformers and local priors of CNNs to simultaneously extract global and local forgery clues, and novel MoE modules are designed to scale the model's capacity and select optimal forgery experts, further enhancing forgery detection performance.

Abstract

The paper presents MoE-FFD, a generalized and parameter-efficient approach for face forgery detection. The key highlights are:

1. MoE-FFD integrates lightweight LoRA and Adapter layers with the frozen ViT backbone, enabling parameter-efficient training by only updating the external modules while preserving the ImageNet knowledge.

2. The designed LoRA layers capture long-range interactions within input faces, while the Convpass Adapter layers effectively highlight local forgery anomalies. This combination leverages the expressivity of transformers and the local forgery priors of CNNs, leading to enhanced generalizability and robustness.

3. Novel MoE modules are introduced in both LoRA and Adapter layers to scale the model capacity and dynamically select optimal forgery experts for input faces, further boosting the detection performance.

4. Extensive experiments on six Deepfake datasets and various perturbations demonstrate that MoE-FFD achieves state-of-the-art generalizability and robustness, while using significantly fewer activated parameters compared to previous methods.

Stats

MoE-FFD with the fewest activated parameters (15.51M) achieves the best AUC score on the unseen CelebDF-v2 dataset.
MoE-FFD outperforms the ViT-B baseline by 18.5% in average AUC score on cross-manipulation evaluations.
MoE-FFD exhibits significantly greater resilience to common perturbations compared to previous methods.

Quotes

"MoE-FFD only updates external lightweight LoRA and Adapter parameters while keeping the ViT backbone frozen, thereby achieving parameter-efficient training."
"The designed LoRA layers capture long-range interactions within input faces, while the Convpass Adapter layers effectively highlight local forgery anomalies."
"Novel MoE modules are introduced in both LoRA and Adapter layers to scale the model capacity and dynamically select optimal forgery experts for input faces."

Key Insights Distilled From

MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detection

by Chenqi Kong,... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08452.pdf

Deeper Inquiries

How can the proposed MoE-FFD framework be extended to other computer vision tasks beyond face forgery detection

The proposed MoE-FFD framework can be extended to other computer vision tasks by leveraging its key components and design principles. One way to extend this framework is to apply it to image classification tasks. By integrating the Mixture of Experts (MoE) modules with different backbone architectures, such as ResNet or EfficientNet, the model can dynamically select optimal experts for feature extraction, leading to improved classification performance. Additionally, the parameter-efficient training strategy used in MoE-FFD can be beneficial in scenarios where computational resources are limited, making it suitable for real-time applications like object detection or image segmentation. Furthermore, the adaptability of MoE-FFD to different transformer backbones allows for seamless integration into various computer vision tasks, enhancing model generalizability and robustness across different domains.

What are the potential limitations of the MoE-FFD approach, and how can they be addressed in future research

While the MoE-FFD approach offers significant advantages in face forgery detection, there are potential limitations that need to be addressed in future research. One limitation is the reliance on pre-trained ImageNet weights for the ViT backbone, which may not always capture domain-specific features relevant to face forgery detection. To overcome this limitation, future research could explore domain-specific pre-training or transfer learning strategies to enhance the model's ability to detect forged faces accurately. Additionally, the selection of optimal experts in the MoE modules may still be prone to biases or overfitting, requiring further investigation into more robust gating mechanisms or expert selection strategies. Addressing these limitations can improve the model's performance and generalizability across diverse datasets and manipulation types.

Given the importance of face forgery detection, how can the insights from this work be leveraged to develop more efficient and robust detection systems for real-world deployment

The insights from this work on face forgery detection can be leveraged to develop more efficient and robust detection systems for real-world deployment in several ways. Firstly, the parameter-efficient training approach used in MoE-FFD can be applied to optimize resource utilization and reduce computational costs in deploying face forgery detection systems on edge devices or in cloud environments. This can enhance the scalability and accessibility of detection systems for widespread use. Secondly, the integration of MoE modules for dynamic expert selection can be extended to enhance the performance of existing face forgery detection systems by improving feature extraction and model adaptability. By incorporating the learnings from MoE-FFD, researchers and practitioners can develop more advanced and reliable face forgery detection systems that are capable of detecting increasingly sophisticated deepfake content with high accuracy and efficiency.