Abstract
Ensuring the authenticity of video content remains challenging as DeepFake generation becomes increasingly realistic and robust against detection. Most existing detectors implicitly assume temporally consistent and clean facial sequences, an assumption that rarely holds in real-world scenarios where compression artifacts, occlusions, and adversarial attacks destabilize face detection and often lead to invalid or misdetected faces. To address these challenges, we propose a Laplacian-Regularized Graph Convolutional Network (LR-GCN) that robustly detects DeepFakes from noisy or unordered face sequences, while being trained only on clean facial data. Our method constructs an Order-Free Temporal Graph Embedding (OF-TGE) that organizes frame-wise CNN features into an adaptive sparse graph based on semantic affinities. Unlike traditional methods constrained by strict temporal continuity, OF-TGE captures intrinsic feature consistency across frames, making it resilient to shuffled, missing, or heavily corrupted inputs. We further impose a dual-level sparsity mechanism on both graph structure and node features to suppress the influence of invalid faces. Crucially, we introduce an explicit Graph Laplacian Spectral Prior that acts as a high-pass operator in the graph spectral domain, highlighting structural anomalies and forgery artifacts, which are then consolidated by a low-pass GCN aggregation. This sequential design effectively realizes a task-driven spectral band-pass mechanism that suppresses background information and random noise while preserving manipulation cues. Extensive experiments on FF++, Celeb-DFv2, and DFDC demonstrate that LR-GCN achieves state-of-the-art performance and significantly improved robustness under severe global and local disruptions, including missing faces, occlusions, and adversarially perturbed face detections.
Abstract (translated)
确保视频内容的真实性仍然是一项挑战，因为DeepFake生成变得越来越逼真，并且在检测上也越来越具有抵抗力。现有的大多数检测器都隐含地假设面部序列是时间一致且清晰的，而在现实场景中这一假设很少成立：由于压缩失真、遮挡和对抗性攻击的影响，人脸检测经常不稳定并导致无效或误检的人脸。为了解决这些问题，我们提出了一种拉普拉斯正则化图卷积网络（LR-GCN），该方法可以在仅使用干净面部数据进行训练的情况下，从嘈杂的或无序的面部序列中稳健地检测DeepFakes。 我们的方法构建了一个顺序无关的时间图嵌入（OF-TGE），它将每帧的CNN特征组织成一个基于语义相似性的自适应稀疏图。与受严格时间连续性约束的传统方法不同，OF-TGE捕获了跨帧的固有特征一致性，使其能够应对打乱、缺失或严重损坏的数据输入。 我们进一步在图结构和节点特征上引入了一种双层稀疏机制来抑制无效面部的影响。关键的是，我们提出了一种明确的图拉普拉斯谱先验，它在图频域中起高通滤波器的作用，突出显示结构性异常和伪造痕迹，并通过低通GCN聚合进行整合。 这种顺序设计有效地实现了任务驱动的频段带通机制，在抑制背景信息和随机噪声的同时保留操作线索。在FF++、Celeb-DFv2 和 DFDC 数据集上的广泛实验表明，LR-GCN 达到了最先进的性能并显著提高了对严重全局和局部干扰（包括缺失面部、遮挡以及被对抗性扰动的面部检测）的鲁棒性。
URL
https://arxiv.org/abs/2512.07498
