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.
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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。
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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。
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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)。
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由于其在保护面部识别系统免于演示攻击(PAS)中的至关重要的作用,因此面部抗散热器(FAS)最近引起了人们的关注。随着越来越现实的PA随着新颖类型的发展,由于其表示能力有限,基于手工特征的传统FAS方法变得不可靠。随着近十年来大规模学术数据集的出现,基于深度学习的FA实现了卓越的性能并占据了这一领域。但是,该领域的现有评论主要集中在手工制作的功能上,这些功能过时,对FAS社区的进步没有任何启发。在本文中,为了刺激未来的研究,我们对基于深度学习的FAS的最新进展进行了首次全面综述。它涵盖了几个新颖且有见地的组成部分:1)除了使用二进制标签的监督(例如,``0'''for pas vs.'1'),我们还通过像素智能监督(例如,伪深度图)调查了最新方法; 2)除了传统的数据内评估外,我们还收集和分析专门为域概括和开放式FAS设计的最新方法; 3)除了商用RGB摄像机外,我们还总结了多模式(例如,深度和红外线)或专门(例如,光场和闪存)传感器下的深度学习应用程序。我们通过强调当前的开放问题并突出潜在的前景来结束这项调查。
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随着信息中的各种方式存在于现实世界中的各种方式,多式联信息之间的有效互动和融合在计算机视觉和深度学习研究中的多模式数据的创造和感知中起着关键作用。通过卓越的功率,在多式联运信息中建模互动,多式联运图像合成和编辑近年来已成为一个热门研究主题。与传统的视觉指导不同,提供明确的线索,多式联路指南在图像合成和编辑方面提供直观和灵活的手段。另一方面,该领域也面临着具有固有的模态差距的特征的几个挑战,高分辨率图像的合成,忠实的评估度量等。在本调查中,我们全面地阐述了最近多式联运图像综合的进展根据数据模型和模型架构编辑和制定分类。我们从图像合成和编辑中的不同类型的引导方式开始介绍。然后,我们描述了多模式图像综合和编辑方法,其具有详细的框架,包括生成的对抗网络(GAN),GaN反转,变压器和其他方法,例如NERF和扩散模型。其次是在多模式图像合成和编辑中广泛采用的基准数据集和相应的评估度量的综合描述,以及分析各个优点和限制的不同合成方法的详细比较。最后,我们为目前的研究挑战和未来的研究方向提供了深入了解。与本调查相关的项目可在HTTPS://github.com/fnzhan/mise上获得
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深度神经网络在人类分析中已经普遍存在,增强了应用的性能,例如生物识别识别,动作识别以及人重新识别。但是,此类网络的性能通过可用的培训数据缩放。在人类分析中,对大规模数据集的需求构成了严重的挑战,因为数据收集乏味,廉价,昂贵,并且必须遵守数据保护法。当前的研究研究了\ textit {合成数据}的生成,作为在现场收集真实数据的有效且具有隐私性的替代方案。这项调查介绍了基本定义和方法,在生成和采用合成数据进行人类分析时必不可少。我们进行了一项调查,总结了当前的最新方法以及使用合成数据的主要好处。我们还提供了公开可用的合成数据集和生成模型的概述。最后,我们讨论了该领域的局限性以及开放研究问题。这项调查旨在为人类分析领域的研究人员和从业人员提供。
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With the rapid development of deep generative models (such as Generative Adversarial Networks and Auto-encoders), AI-synthesized images of the human face are now of such high quality that humans can hardly distinguish them from pristine ones. Although existing detection methods have shown high performance in specific evaluation settings, e.g., on images from seen models or on images without real-world post-processings, they tend to suffer serious performance degradation in real-world scenarios where testing images can be generated by more powerful generation models or combined with various post-processing operations. To address this issue, we propose a Global and Local Feature Fusion (GLFF) to learn rich and discriminative representations by combining multi-scale global features from the whole image with refined local features from informative patches for face forgery detection. GLFF fuses information from two branches: the global branch to extract multi-scale semantic features and the local branch to select informative patches for detailed local artifacts extraction. Due to the lack of a face forgery dataset simulating real-world applications for evaluation, we further create a challenging face forgery dataset, named DeepFakeFaceForensics (DF^3), which contains 6 state-of-the-art generation models and a variety of post-processing techniques to approach the real-world scenarios. Experimental results demonstrate the superiority of our method to the state-of-the-art methods on the proposed DF^3 dataset and three other open-source datasets.
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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。
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最近,面部生物识别是对传统认证系统的方便替代的巨大关注。因此,检测恶意尝试已经发现具有重要意义,导致面部抗欺骗〜(FAS),即面部呈现攻击检测。与手工制作的功能相反,深度特色学习和技术已经承诺急剧增加FAS系统的准确性,解决了实现这种系统的真实应用的关键挑战。因此,处理更广泛的发展以及准确的模型的新研究区越来越多地引起了研究界和行业的关注。在本文中,我们为自2017年以来对与基于深度特征的FAS方法相关的文献综合调查。在这一主题上阐明,基于各种特征和学习方法的语义分类。此外,我们以时间顺序排列,其进化进展和评估标准(数据集内集和数据集互联集合中集)覆盖了FAS的主要公共数据集。最后,我们讨论了开放的研究挑战和未来方向。
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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。
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本文的目标是对面部素描合成(FSS)问题进行全面的研究。然而,由于获得了手绘草图数据集的高成本,因此缺乏完整的基准,用于评估过去十年的FSS算法的开发。因此,我们首先向FSS引入高质量的数据集,名为FS2K,其中包括2,104个图像素描对,跨越三种类型的草图样式,图像背景,照明条件,肤色和面部属性。 FS2K与以前的FSS数据集不同于难度,多样性和可扩展性,因此应促进FSS研究的进展。其次,我们通过调查139种古典方法,包括34个手工特征的面部素描合成方法,37个一般的神经式传输方法,43个深映像到图像翻译方法,以及35个图像 - 素描方法。此外,我们详细说明了现有的19个尖端模型的综合实验。第三,我们为FSS提供了一个简单的基准,名为FSGAN。只有两个直截了当的组件,即面部感知屏蔽和风格矢量扩展,FSGAN将超越所提出的FS2K数据集的所有先前最先进模型的性能,通过大边距。最后,我们在过去几年中汲取的经验教训,并指出了几个未解决的挑战。我们的开源代码可在https://github.com/dengpingfan/fsgan中获得。
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Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems. Among their salient benefits, Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence as compared to recurrent networks e.g., Long short-term memory (LSTM). Different from convolutional networks, Transformers require minimal inductive biases for their design and are naturally suited as set-functions. Furthermore, the straightforward design of Transformers allows processing multiple modalities (e.g., images, videos, text and speech) using similar processing blocks and demonstrates excellent scalability to very large capacity networks and huge datasets. These strengths have led to exciting progress on a number of vision tasks using Transformer networks. This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline. We start with an introduction to fundamental concepts behind the success of Transformers i.e., self-attention, large-scale pre-training, and bidirectional feature encoding. We then cover extensive applications of transformers in vision including popular recognition tasks (e.g., image classification, object detection, action recognition, and segmentation), generative modeling, multi-modal tasks (e.g., visual-question answering, visual reasoning, and visual grounding), video processing (e.g., activity recognition, video forecasting), low-level vision (e.g., image super-resolution, image enhancement, and colorization) and 3D analysis (e.g., point cloud classification and segmentation). We compare the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value. Finally, we provide an analysis on open research directions and possible future works. We hope this effort will ignite further interest in the community to solve current challenges towards the application of transformer models in computer vision.
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由于滥用了深层,检测伪造视频是非常可取的。现有的检测方法有助于探索DeepFake视频中的特定工件,并且非常适合某些数据。但是,这些人工制品的不断增长的技术一直在挑战传统的深泡探测器的鲁棒性。结果,这些方法的普遍性的发展已达到阻塞。为了解决这个问题,鉴于经验结果是,深层视频中经常在声音和面部背后的身份不匹配,并且声音和面孔在某种程度上具有同质性,在本文中,我们建议从未开发的语音中执行深层检测 - 面对匹配视图。为此,设计了一种语音匹配方法来测量这两个方法的匹配度。然而,对特定的深泡数据集进行培训使模型过于拟合深层算法的某些特征。相反,我们提倡一种迅速适应未开发的伪造方法的方法,然后进行预训练,然后进行微调范式。具体而言,我们首先在通用音频视频数据集上预先培训该模型,然后在下游深板数据上进行微调。我们对三个广泛利用的DeepFake数据集进行了广泛的实验-DFDC,Fakeavceleb和DeepFaketimit。与其他最先进的竞争对手相比,我们的方法获得了显着的性能增长。还值得注意的是,我们的方法在有限的DeepFake数据上进行了微调时已经取得了竞争性结果。
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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.
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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.
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Our goal with this survey is to provide an overview of the state of the art deep learning technologies for face generation and editing. We will cover popular latest architectures and discuss key ideas that make them work, such as inversion, latent representation, loss functions, training procedures, editing methods, and cross domain style transfer. We particularly focus on GAN-based architectures that have culminated in the StyleGAN approaches, which allow generation of high-quality face images and offer rich interfaces for controllable semantics editing and preserving photo quality. We aim to provide an entry point into the field for readers that have basic knowledge about the field of deep learning and are looking for an accessible introduction and overview.
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面部超分辨率(FSR),也称为面部幻觉,其旨在增强低分辨率(LR)面部图像以产生高分辨率(HR)面部图像的分辨率,是特定于域的图像超分辨率问题。最近,FSR获得了相当大的关注,并目睹了深度学习技术的发展炫目。迄今为止,有很少有基于深入学习的FSR的研究摘要。在本次调查中,我们以系统的方式对基于深度学习的FSR方法进行了全面审查。首先,我们总结了FSR的问题制定,并引入了流行的评估度量和损失功能。其次,我们详细说明了FSR中使用的面部特征和流行数据集。第三,我们根据面部特征的利用大致分类了现有方法。在每个类别中,我们从设计原则的一般描述开始,然后概述代表方法,然后讨论其中的利弊。第四,我们评估了一些最先进的方法的表现。第五,联合FSR和其他任务以及与FSR相关的申请大致介绍。最后,我们设想了这一领域进一步的技术进步的前景。在\ URL {https://github.com/junjun-jiang/face-hallucination-benchmark}上有一个策划的文件和资源的策划文件和资源清单
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Face Restoration (FR) aims to restore High-Quality (HQ) faces from Low-Quality (LQ) input images, which is a domain-specific image restoration problem in the low-level computer vision area. The early face restoration methods mainly use statistic priors and degradation models, which are difficult to meet the requirements of real-world applications in practice. In recent years, face restoration has witnessed great progress after stepping into the deep learning era. However, there are few works to study deep learning-based face restoration methods systematically. Thus, this paper comprehensively surveys recent advances in deep learning techniques for face restoration. Specifically, we first summarize different problem formulations and analyze the characteristic of the face image. Second, we discuss the challenges of face restoration. Concerning these challenges, we present a comprehensive review of existing FR methods, including prior based methods and deep learning-based methods. Then, we explore developed techniques in the task of FR covering network architectures, loss functions, and benchmark datasets. We also conduct a systematic benchmark evaluation on representative methods. Finally, we discuss future directions, including network designs, metrics, benchmark datasets, applications,etc. We also provide an open-source repository for all the discussed methods, which is available at https://github.com/TaoWangzj/Awesome-Face-Restoration.
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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。
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