GRACE：基于拉普拉斯平滑的图正则化注意力卷积纠缠用于鲁棒DeepFake视频检测
GRACE: Graph-Regularized Attentive Convolutional Entanglement with Laplacian Smoothing for Robust DeepFake Video Detection
AI总结：
本文提出GRACE方法，通过图卷积网络结合图拉普拉斯先验和稀疏约束，将时空特征相互纠缠并去除噪声，以提升噪声面部序列下DeepFake视频检测的鲁棒性。
摘要：
随着DeepFake视频篡改技术不断升级并构成严重威胁，开发高效检测策略的紧迫性日益凸显。然而，一个突出问题在于面部图像被误检，这通常源于视频质量退化或对抗攻击，导致出现意外的时间伪影，从而削弱DeepFake视频检测技术的有效性。本文提出了一种用于鲁棒DeepFake视频检测的新方法，利用所提出的基于图卷积网络与图拉普拉斯的图正则化注意力卷积纠缠（GRACE）来应对上述挑战。首先，采用传统卷积神经网络对整个视频提取时空特征。然后，通过构建具有稀疏约束的图，将空间特征与时间特征相互纠缠，强制保留噪声面部序列中有效人脸图像的关键特征，从而增强DeepFake视频检测的稳定性和性能。此外，在图卷积网络中引入图拉普拉斯先验，以去除特征空间中的噪声模式，进一步提升性能。综合实验表明，所提方法在噪声面部序列下的DeepFake视频检测中达到了最先进的性能。源代码可在 https://github.com/ming053l/GRACE 获取。
英文摘要：
As DeepFake video manipulation techniques escalate, posing profound threats, the urgent need to develop efficient detection strategies is underscored. However, one particular issue lies with facial images being mis-detected, often originating from degraded videos or adversarial attacks, leading to unexpected temporal artifacts that can undermine the efficacy of DeepFake video detection techniques. This paper introduces a novel method for robust DeepFake video detection, harnessing the power of the proposed Graph-Regularized Attentive Convolutional Entanglement (GRACE) based on the graph convolutional network with graph Laplacian to address the aforementioned challenges. First, conventional Convolution Neural Networks are deployed to perform spatiotemporal features for the entire video. Then, the spatial and temporal features are mutually entangled by constructing a graph with sparse constraint, enforcing essential features of valid face images in the noisy face sequences remaining, thus augmenting stability and performance for DeepFake video detection. Furthermore, the Graph Laplacian prior is proposed in the graph convolutional network to remove the noise pattern in the feature space to further improve the performance. Comprehensive experiments are conducted to illustrate that our proposed method delivers state-of-the-art performance in DeepFake video detection under noisy face sequences. The source code is available at https://github.com/ming053l/GRACE.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
