Deep-learning-based technologies such as deepfakes ones have been attracting widespread attention in both society and academia, particularly ones used to synthesize forged face images. These automatic and professional-skill-free face manipulation technologies can be used to replace the face in an original image or video with any target object while maintaining the expression and demeanor. Since human faces are closely related to identity characteristics, maliciously disseminated identity manipulated videos could trigger a crisis of public trust in the media and could even have serious political, social, and legal implications. To effectively detect manipulated videos, we focus on the position offset in the face blending process, resulting from the forced affine transformation of the normalized forged face. We introduce a method for detecting manipulated videos that is based on the trajectory of the facial region displacement. Specifically, we develop a virtual-anchor-based method for extracting the facial trajectory, which can robustly represent displacement information. This information was used to construct a network for exposing multidimensional artifacts in the trajectory sequences of manipulated videos that is based on dual-stream spatial-temporal graph attention and a gated recurrent unit backbone. Testing of our method on various manipulation datasets demonstrated that its accuracy and generalization ability is competitive with that of the leading detection methods.

随着生成模型的快速发展，基于AI的面部操纵技术，称为DeepFakes，已经变得越来越真实。这种脸部伪造的方法可以攻击任何目标，这对个人隐私和财产安全构成了新的威胁。此外，滥用合成视频在许多领域都显示出潜在的危险，例如身份骚扰，色情和新闻谣言。受到生理信号中的空间相干性和时间一致性在所生物的内容中被破坏的事实，我们试图找到可以区分真实视频和合成视频的不一致模式，从面部像素的变化是与生理信息高度相关的。我们的方法首先将多个高斯级别的eulerian视频放大倍数（EVM）应用于原始视频，以扩大面部血容量的变化引起的生理变化，然后将原始视频和放大的视频转换为多尺度欧拉宽度的空间 - 时间地图（MemstMap），其可以代表不同八度的时变的生理增强序列。然后，这些地图以列为单位重新装入帧修补程序，并发送到视觉变压器以学习帧级别的时空描述符。最后，我们整理了嵌入功能并输出判断视频是真实还是假的概率。我们在面部框架++和DeepFake检测数据集上验证了我们的方法。结果表明，我们的模型在伪造检测中实现了出色的性能，并在交叉数据域中显示出出色的泛化能力。

随着面部伪造技术的快速发展，DeepFake视频在数字媒体上引起了广泛的关注。肇事者大量利用这些视频来传播虚假信息并发表误导性陈述。大多数现有的DeepFake检测方法主要集中于纹理特征，纹理特征可能会受到外部波动（例如照明和噪声）的影响。此外，基于面部地标的检测方法对外部变量更强大，但缺乏足够的细节。因此，如何在空间，时间和频域中有效地挖掘独特的特征，并将其与面部地标融合以进行伪造视频检测仍然是一个悬而未决的问题。为此，我们提出了一个基于多种模式的信息和面部地标的几何特征，提出了地标增强的多模式图神经网络（LEM-GNN）。具体而言，在框架级别上，我们设计了一种融合机制来挖掘空间和频域元素的联合表示，同时引入几何面部特征以增强模型的鲁棒性。在视频级别，我们首先将视频中的每个帧视为图中的节点，然后将时间信息编码到图表的边缘。然后，通过应用图形神经网络（GNN）的消息传递机制，将有效合并多模式特征，以获得视频伪造的全面表示。广泛的实验表明，我们的方法始终优于广泛使用的基准上的最先进（SOTA）。

Online media data, in the forms of images and videos, are becoming mainstream communication channels. However, recent advances in deep learning, particularly deep generative models, open the doors for producing perceptually convincing images and videos at a low cost, which not only poses a serious threat to the trustworthiness of digital information but also has severe societal implications. This motivates a growing interest of research in media tampering detection, i.e., using deep learning techniques to examine whether media data have been maliciously manipulated. Depending on the content of the targeted images, media forgery could be divided into image tampering and Deepfake techniques. The former typically moves or erases the visual elements in ordinary images, while the latter manipulates the expressions and even the identity of human faces. Accordingly, the means of defense include image tampering detection and Deepfake detection, which share a wide variety of properties. In this paper, we provide a comprehensive review of the current media tampering detection approaches, and discuss the challenges and trends in this field for future research.

深度学习已成功地用于解决从大数据分析到计算机视觉和人级控制的各种复杂问题。但是，还采用了深度学习进步来创建可能构成隐私，民主和国家安全威胁的软件。最近出现的那些深度学习驱动的应用程序之一是Deepfake。 DeepFake算法可以创建人类无法将它们与真实图像区分开的假图像和视频。因此，可以自动检测和评估数字视觉媒体完整性的技术的建议是必不可少的。本文介绍了一项用于创造深击的算法的调查，更重要的是，提出的方法旨在检测迄今为止文献中的深击。我们对与Deepfake技术有关的挑战，研究趋势和方向进行了广泛的讨论。通过回顾深层味和最先进的深层检测方法的背景，本研究提供了深入的深层技术的概述，并促进了新的，更强大的方法的发展，以应对日益挑战性的深击。

Deep learning has enabled realistic face manipulation (i.e., deepfake), which poses significant concerns over the integrity of the media in circulation. Most existing deep learning techniques for deepfake detection can achieve promising performance in the intra-dataset evaluation setting (i.e., training and testing on the same dataset), but are unable to perform satisfactorily in the inter-dataset evaluation setting (i.e., training on one dataset and testing on another). Most of the previous methods use the backbone network to extract global features for making predictions and only employ binary supervision (i.e., indicating whether the training instances are fake or authentic) to train the network. Classification merely based on the learning of global features leads often leads to weak generalizability to unseen manipulation methods. In addition, the reconstruction task can improve the learned representations. In this paper, we introduce a novel approach for deepfake detection, which considers the reconstruction and classification tasks simultaneously to address these problems. This method shares the information learned by one task with the other, which focuses on a different aspect other existing works rarely consider and hence boosts the overall performance. In particular, we design a two-branch Convolutional AutoEncoder (CAE), in which the Convolutional Encoder used to compress the feature map into the latent representation is shared by both branches. Then the latent representation of the input data is fed to a simple classifier and the unsupervised reconstruction component simultaneously. Our network is trained end-to-end. Experiments demonstrate that our method achieves state-of-the-art performance on three commonly-used datasets, particularly in the cross-dataset evaluation setting.

近年来，随着面部编辑和发电的迅速发展，越来越多的虚假视频正在社交媒体上流传，这引起了极端公众的关注。基于频域的现有面部伪造方法发现，与真实图像相比，GAN锻造图像在频谱中具有明显的网格视觉伪像。但是对于综合视频，这些方法仅局限于单个帧，几乎不关注不同框架之间最歧视的部分和时间频率线索。为了充分利用视频序列中丰富的信息，本文对空间和时间频域进行了视频伪造检测，并提出了一个离散的基于余弦转换的伪造线索增强网络（FCAN-DCT），以实现更全面的时空功能表示。 FCAN-DCT由一个骨干网络和两个分支组成：紧凑特征提取（CFE）模块和频率时间注意（FTA）模块。我们对两个可见光（VIS）数据集Wilddeepfake和Celeb-DF（V2）进行了彻底的实验评估，以及我们的自我构建的视频伪造数据集DeepFakenir，这是第一个近境模式的视频伪造数据集。实验结果证明了我们方法在VIS和NIR场景中检测伪造视频的有效性。

As ultra-realistic face forgery techniques emerge, deepfake detection has attracted increasing attention due to security concerns. Many detectors cannot achieve accurate results when detecting unseen manipulations despite excellent performance on known forgeries. In this paper, we are motivated by the observation that the discrepancies between real and fake videos are extremely subtle and localized, and inconsistencies or irregularities can exist in some critical facial regions across various information domains. To this end, we propose a novel pipeline, Cross-Domain Local Forensics (XDLF), for more general deepfake video detection. In the proposed pipeline, a specialized framework is presented to simultaneously exploit local forgery patterns from space, frequency, and time domains, thus learning cross-domain features to detect forgeries. Moreover, the framework leverages four high-level forgery-sensitive local regions of a human face to guide the model to enhance subtle artifacts and localize potential anomalies. Extensive experiments on several benchmark datasets demonstrate the impressive performance of our method, and we achieve superiority over several state-of-the-art methods on cross-dataset generalization. We also examined the factors that contribute to its performance through ablations, which suggests that exploiting cross-domain local characteristics is a noteworthy direction for developing more general deepfake detectors.

现在，合成视觉媒体发电和操纵的加速增长已经达到了引起重大关注并对社会造成巨大恐吓的地步。当务之急需要自动检测网络涉及虚假数字内容，并避免危险人造信息的传播以应对这种威胁。在本文中，我们利用和比较了两种手工制作的功能（Sift和Hog）以及两种深层特征（Xpection和CNN+RNN），以进行深层捕获检测任务。当训练集和测试集之间存在不匹配时，我们还会检查这些功能的性能。评估是对著名的FaceForensics ++数据集进行的，该数据集包含四个子数据集，深盘，face2face，faceswap和neuralTextures。最好的结果来自Xception，当训练和测试集都来自同一子数据库时，精度可能会超过99 \％。相比之下，当训练集不匹配测试集时，结果急剧下降。这种现象揭示了创建通用深击检测系统的挑战。

本文提出了一种基于图像中出现的边缘区域特征的广义和鲁棒的面部操纵检测方法。大多数现代面部合成过程包括颜色尴尬降低，但损坏了边缘区域中的自然指纹。另外，这些颜色校正过程不会在非面部背景区域中进行。我们还观察到合成过程不考虑时域中出现的图像的自然属性。考虑到这些观察，我们提出了一种面部法医框架，其利用在整个图像的边缘区域中出现的像素级颜色特征。此外，我们的框架包括一个3D-CNN分类模型，其在空间和时间上解释提取的颜色特征。与其他现有研究不同，我们通过考虑从一个视频内从多个帧中提取的所有功能来进行真实性确定。通过广泛的实验，包括真实世界的情景来评估广义检测能力，我们表明我们的框架在准确性和稳健性方面优于最先进的面部操纵检测技术。

近年来，视觉伪造达到了人类无法识别欺诈的复杂程度，这对信息安全构成了重大威胁。出现了广泛的恶意申请，例如名人的假新闻，诽谤或勒索，政治战中的政治家冒充，以及谣言的传播吸引观点。结果，已经提出了一种富有的视觉验证技术，以试图阻止这种危险的趋势。在本文中，我们使用全面的和经验方法，提供了一种基准，可以对视觉伪造和视觉取证进行深入的洞察。更具体地，我们开发一个独立的框架，整合最先进的假冒生成器和探测器，并使用各种标准来测量这些技术的性能。我们还对基准测试结果进行了详尽的分析，确定了在措施与对策之间永无止境的战争中的比较参考的方法的特征。

尽管令人鼓舞的是深泡检测的进展，但由于训练过程中探索的伪造线索有限，对未见伪造类型的概括仍然是一个重大挑战。相比之下，我们注意到Deepfake中的一种常见现象：虚假的视频创建不可避免地破坏了原始视频中的统计规律性。受到这一观察的启发，我们建议通过区分实际视频中没有出现的“规律性中断”来增强深层检测的概括。具体而言，通过仔细检查空间和时间属性，我们建议通过伪捕获生成器破坏真实的视频，并创建各种伪造视频以供培训。这种做法使我们能够在不使用虚假视频的情况下实现深泡沫检测，并以简单有效的方式提高概括能力。为了共同捕获空间和时间上的破坏，我们提出了一个时空增强块，以了解我们自我创建的视频之间的规律性破坏。通过全面的实验，我们的方法在几个数据集上表现出色。

AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information. The need to develop and evaluate DeepFake detection algorithms calls for large-scale datasets. However, current DeepFake datasets suffer from low visual quality and do not resemble Deep-Fake videos circulated on the Internet. We present a new large-scale challenging DeepFake video dataset, Celeb-DF, which contains 5, 639 high-quality DeepFake videos of celebrities generated using improved synthesis process. We conduct a comprehensive evaluation of DeepFake detection methods and datasets to demonstrate the escalated level of challenges posed by Celeb-DF.

微表达（MES）是非自愿的面部运动，揭示了人们在高利害情况下隐藏的感受，并对医疗，国家安全，审讯和许多人机交互系统具有实际重要性。早期的MER方法主要基于传统的外观和几何特征。最近，随着各种领域的深度学习（DL）的成功，神经网络已得到MER的兴趣。不同于宏观表达，MES是自发的，微妙的，快速的面部运动，导致数据收集困难，因此具有小规模的数据集。由于上述我的角色，基于DL的MER变得挑战。迄今为止，已提出各种DL方法来解决我的问题并提高MER表现。在本调查中，我们对深度微表达识别（MER）进行了全面的审查，包括数据集，深度MER管道和最具影响力方法的基准标记。本调查定义了该领域的新分类法，包括基于DL的MER的所有方面。对于每个方面，总结和讨论了基本方法和高级发展。此外，我们得出了坚固的深层MER系统设计的剩余挑战和潜在方向。据我们所知，这是对深度MEL方法的第一次调查，该调查可以作为未来MER研究的参考点。

Figure 1: FaceForensics++ is a dataset of facial forgeries that enables researchers to train deep-learning-based approaches in a supervised fashion. The dataset contains manipulations created with four state-of-the-art methods, namely, Face2Face, FaceSwap, DeepFakes, and NeuralTextures.

本文介绍了我们关于使用时间图像进行深泡探测的结果和发现。我们通过使用这些面部地标上的像素值构造图像（称为时间图像），模拟了在给定视频跨帧的468个面部标志物横跨给定视频框架中的临时关系。CNN能够识别给定图像的像素之间存在的空间关系。研究了10种不同的成像网模型。

In this paper, we introduce MINTIME, a video deepfake detection approach that captures spatial and temporal anomalies and handles instances of multiple people in the same video and variations in face sizes. Previous approaches disregard such information either by using simple a-posteriori aggregation schemes, i.e., average or max operation, or using only one identity for the inference, i.e., the largest one. On the contrary, the proposed approach builds on a Spatio-Temporal TimeSformer combined with a Convolutional Neural Network backbone to capture spatio-temporal anomalies from the face sequences of multiple identities depicted in a video. This is achieved through an Identity-aware Attention mechanism that attends to each face sequence independently based on a masking operation and facilitates video-level aggregation. In addition, two novel embeddings are employed: (i) the Temporal Coherent Positional Embedding that encodes each face sequence's temporal information and (ii) the Size Embedding that encodes the size of the faces as a ratio to the video frame size. These extensions allow our system to adapt particularly well in the wild by learning how to aggregate information of multiple identities, which is usually disregarded by other methods in the literature. It achieves state-of-the-art results on the ForgeryNet dataset with an improvement of up to 14% AUC in videos containing multiple people and demonstrates ample generalization capabilities in cross-forgery and cross-dataset settings. The code is publicly available at https://github.com/davide-coccomini/MINTIME-Multi-Identity-size-iNvariant-TIMEsformer-for-Video-Deepfake-Detection.

Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, or heart fluttering, and is therefore of particular interest. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning. It providing a taxonomy based on the factors that divide anomaly detection models into different categories. Aside from describing the basic anomaly detection technique for each category, the advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. It finally summarises open issues in research and challenges faced while adopting deep anomaly detection models.

随着过去五年的快速发展，面部身份验证已成为最普遍的生物识别方法。得益于高准确的识别性能和用户友好的用法，自动面部识别（AFR）已爆炸成多次实用的应用程序，而不是设备解锁，签到和经济支付。尽管面部身份验证取得了巨大的成功，但各种面部表现攻击（FPA），例如印刷攻击，重播攻击和3D面具攻击，但仍引起了不信任的问题。除了身体上的攻击外，面部视频/图像很容易受到恶意黑客发起的各种数字攻击技术的影响，从而对整个公众造成了潜在的威胁。由于无限制地访问了巨大的数字面部图像/视频，并披露了互联网上流通的易于使用的面部操纵工具，因此没有任何先前专业技能的非专家攻击者能够轻松创建精致的假面，从而导致许多危险的应用程序例如财务欺诈，模仿和身份盗用。这项调查旨在通过提供对现有文献的彻底分析并突出需要进一步关注的问题来建立面部取证的完整性。在本文中，我们首先全面调查了物理和数字面部攻击类型和数据集。然后，我们回顾了现有的反攻击方法的最新和最先进的进度，并突出显示其当前限制。此外，我们概述了面对法医社区中现有和即将面临的挑战的未来研究指示。最后，已经讨论了联合物理和数字面部攻击检​​测的必要性，这在先前的调查中从未进行过研究。

最近，由于社交媒体数字取证中的安全性和隐私问题，DeepFake引起了广泛的公众关注。随着互联网上广泛传播的深层视频变得越来越现实，传统的检测技术未能区分真实和假货。大多数现有的深度学习方法主要集中于使用卷积神经网络作为骨干的局部特征和面部图像中的关系。但是，本地特征和关系不足以用于模型培训，无法学习足够的一般信息以进行深层检测。因此，现有的DeepFake检测方法已达到瓶颈，以进一步改善检测性能。为了解决这个问题，我们提出了一个深度卷积变压器，以在本地和全球范围内纳入决定性图像。具体而言，我们应用卷积池和重新注意事项来丰富提取的特征并增强功效。此外，我们在模型训练中采用了几乎没有讨论的图像关键框架来改进性能，并可视化由视频压缩引起的密钥和正常图像帧之间的特征数量差距。我们最终通过在几个DeepFake基准数据集上进行了广泛的实验来说明可传递性。所提出的解决方案在内部和跨数据库实验上始终优于几个最先进的基线。