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
this https URL

通过引入基于图卷积网络和图拉普拉斯算子的方法，解决了 DeepFake 视频检测中面部图像误检和临时伪影的问题，并在具有噪声面部序列的情况下展现了最先进的性能。

GRACE: 图形正则化注意卷积交捆绑与拉普拉斯平滑用于稳健的深度伪造视频检测

GRACE: Graph-Regularized Attentive Convolutional Entanglement with  Laplacian Smoothing for Robust DeepFake Video Detection

The growing threats of deepfakes to society and cybersecurity have raised
enormous public concerns, and increasing efforts have been devoted to this
critical topic of deepfake video detection. Existing video methods achieve good
performance but are computationally intensive. This paper introduces a simple
yet effective strategy named Thumbnail Layout (TALL), which transforms a video
clip into a pre-defined layout to realize the preservation of spatial and
temporal dependencies. Specifically, consecutive frames are masked in a fixed
position in each frame to improve generalization, then resized to sub-images
and rearranged into a pre-defined layout as the thumbnail. TALL is
model-agnostic and extremely simple by only modifying a few lines of code.
Inspired by the success of vision transformers, we incorporate TALL into Swin
Transformer, forming an efficient and effective method TALL-Swin. Extensive
experiments on intra-dataset and cross-dataset validate the validity and
superiority of TALL and SOTA TALL-Swin. TALL-Swin achieves 90.79$\%$ AUC on the
challenging cross-dataset task, FaceForensics++ $\to$ Celeb-DF. The code is
available at this https URL

本文介绍了一种名为缩略图布局（TALL）的简单而有效的策略，通过将视频剪辑转换为预定义的布局，实现了时空依赖关系的保留，并将该策略与 Swin Transformer 相结合，形成了高效且有效的方法 TALL-Swin，实验表明，TALL-Swin 在跨数据集任务 FaceForensics++ 到 Celeb-DF 上达到了 90.79% 的 AUC 分数。

TALL：深度伪造视频检测的缩略图布局

TALL: Thumbnail Layout for Deepfake Video Detection

Deepfakes have raised significant concerns due to their potential to spread
false information and compromise digital media integrity. In this work, we
propose a Generative Convolutional Vision Transformer (GenConViT) for deepfake
video detection. Our model combines ConvNeXt and Swin Transformer models for
feature extraction, and it utilizes Autoencoder and Variational Autoencoder to
learn from the latent data distribution. By learning from the visual artifacts
and latent data distribution, GenConViT achieves improved performance in
detecting a wide range of deepfake videos. The model is trained and evaluated
on DFDC, FF++, DeepfakeTIMIT, and Celeb-DF v2 datasets, achieving high
classification accuracy, F1 scores, and AUC values. The proposed GenConViT
model demonstrates robust performance in deepfake video detection, with an
average accuracy of 95.8% and an AUC value of 99.3% across the tested datasets.
Our proposed model addresses the challenge of generalizability in deepfake
detection by leveraging visual and latent features and providing an effective
solution for identifying a wide range of fake videos while preserving media
integrity. The code for GenConViT is available at
this https URL

本文提出了一个名为 “GenConViT” 的模型，该模型结合了 ConvNeXt 和 Swin Transformer 模型进行特征提取，并利用自编码器和变分自编码器从潜在数据分布中进行学习，从而在检测各种深度伪造视频方面取得了较好的性能。GenConViT 模型的平均准确度为 95.8％，测试数据集上的 AUC 值为 99.3％，能够有效地解决深度伪造检测中的泛化性挑战。

使用生成卷积视觉转换器进行 Deepfake 视频检测

Deepfake Video Detection Using Generative Convolutional Vision  Transformer

The rapid development of facial manipulation techniques has aroused public
concerns in recent years. Following the success of deep learning, existing
methods always formulate DeepFake video detection as a binary classification
problem and develop frame-based and video-based solutions. However, little
attention has been paid to capturing the spatial-temporal inconsistency in
forged videos. To address this issue, we term this task as a Spatial-Temporal
Inconsistency Learning (STIL) process and instantiate it into a novel STIL
block, which consists of a Spatial Inconsistency Module (SIM), a Temporal
Inconsistency Module (TIM), and an Information Supplement Module (ISM).
Specifically, we present a novel temporal modeling paradigm in TIM by
exploiting the temporal difference over adjacent frames along with both
horizontal and vertical directions. And the ISM simultaneously utilizes the
spatial information from SIM and temporal information from TIM to establish a
more comprehensive spatial-temporal representation. Moreover, our STIL block is
flexible and could be plugged into existing 2D CNNs. Extensive experiments and
visualizations are presented to demonstrate the effectiveness of our method
against the state-of-the-art competitors.

本研究提出了一个基于 Spatial-Temporal Inconsistency Learning (STIL) 的新型 STIL 块，用于改进 DeepFake 视频检测方法，实现更加全面和有效的空间 - 时间表示。通过将 STIL 块灵活地插入到现有的 2D CNN 中，本文通过广泛的实验和可视化结果证明其优于现有的技术方法。