使用社交媒体网站和应用程序已经变得非常受欢迎,人们在这些网络上分享他们的照片。在这些网络上自动识别和标记人们的照片已经提出了隐私保存问题,用户寻求隐藏这些算法的方法。生成的对抗网络(GANS)被证明是非常强大的在高多样性中产生面部图像以及编辑面部图像。在本文中,我们提出了一种基于GAN的生成掩模引导的面部图像操纵(GMFIM)模型,以将无法察觉的编辑应用于输入面部图像以保护图像中的人的隐私。我们的模型由三个主要组件组成:a)面罩模块将面积从输入图像中切断并省略背景,b)用于操纵面部图像并隐藏身份的GaN的优化模块,并覆盖身份和c)用于组合输入图像的背景和操纵的去识别的面部图像的合并模块。在优化步骤的丢失功能中考虑了不同的标准,以产生与输入图像一样类似的高质量图像,同时不能通过AFR系统识别。不同数据集的实验结果表明,与最先进的方法相比,我们的模型可以实现对自动面部识别系统的更好的性能,并且它在大多数实验中捕获更高的攻击成功率。此外,我们提出的模型的产生图像具有最高的质量,更令人愉悦。
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Several face de-identification methods have been proposed to preserve users' privacy by obscuring their faces. These methods, however, can degrade the quality of photos, and they usually do not preserve the utility of faces, e.g., their age, gender, pose, and facial expression. Recently, advanced generative adversarial network models, such as StyleGAN, have been proposed, which generate realistic, high-quality imaginary faces. In this paper, we investigate the use of StyleGAN in generating de-identified faces through style mixing, where the styles or features of the target face and an auxiliary face get mixed to generate a de-identified face that carries the utilities of the target face. We examined this de-identification method with respect to preserving utility and privacy, by implementing several face detection, verification, and identification attacks. Through extensive experiments and also comparing with two state-of-the-art face de-identification methods, we show that StyleGAN preserves the quality and utility of the faces much better than the other approaches and also by choosing the style mixing levels correctly, it can preserve the privacy of the faces much better than other methods.
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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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在线发布的图像存在隐私问题,因为它们可以用作面部识别系统的参考例子。这种滥用的图像违反了隐私权,但很难抵消。很好地确定,可以为基于深神经网络的识别系统创建侵犯示例图像。这些对手示例可用于破坏图像的效用作为参考示例或培训数据。在这项工作中,我们使用生成的对抗性网络(GaN)来创造欺骗面部识别的对抗例子,我们在愚弄面部识别方面取得了可接受的成功率。我们的结果通过去除鉴别器组件来减少GaN的培训时间。此外,我们的结果表明,知识蒸馏可以彻底减少所产生的模型的大小而不会影响性能,表明我们的贡献可以在智能手机上舒适地运行
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生成模型的面部匿名化已经变得越来越普遍,因为它们通过生成虚拟面部图像来消毒私人信息,从而确保隐私和图像实用程序。在删除或保护原始身份后,通常无法识别此类虚拟面部图像。在本文中,我们将生成可识别的虚拟面部图像的问题形式化和解决。我们的虚拟脸部图像在视觉上与原始图像不同,以保护隐私保护。此外,它们具有新的虚拟身份,可直接用于面部识别。我们建议可识别的虚拟面部发电机(IVFG)生成虚拟面部图像。 IVFG根据用户特定的键将原始面部图像的潜在矢量投射到虚拟图像中,该键基于该图像生成虚拟面部图像。为了使虚拟面部图像可识别,我们提出了一个多任务学习目标以及一个三联生的培训策略,以学习IVFG。我们使用不同面部图像数据集上的不同面部识别器评估虚拟面部图像的性能,所有这些都证明了IVFG在生成可识别的虚拟面部图像中的有效性。
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Privacy of machine learning models is one of the remaining challenges that hinder the broad adoption of Artificial Intelligent (AI). This paper considers this problem in the context of image datasets containing faces. Anonymization of such datasets is becoming increasingly important due to their central role in the training of autonomous cars, for example, and the vast amount of data generated by surveillance systems. While most prior work de-identifies facial images by modifying identity features in pixel space, we instead project the image onto the latent space of a Generative Adversarial Network (GAN) model, find the features that provide the biggest identity disentanglement, and then manipulate these features in latent space, pixel space, or both. The main contribution of the paper is the design of a feature-preserving anonymization framework, StyleID, which protects the individuals' identity, while preserving as many characteristics of the original faces in the image dataset as possible. As part of the contribution, we present a novel disentanglement metric, three complementing disentanglement methods, and new insights into identity disentanglement. StyleID provides tunable privacy, has low computational complexity, and is shown to outperform current state-of-the-art solutions.
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The pandemic of these very recent years has led to a dramatic increase in people wearing protective masks in public venues. This poses obvious challenges to the pervasive use of face recognition technology that now is suffering a decline in performance. One way to address the problem is to revert to face recovery methods as a preprocessing step. Current approaches to face reconstruction and manipulation leverage the ability to model the face manifold, but tend to be generic. We introduce a method that is specific for the recovery of the face image from an image of the same individual wearing a mask. We do so by designing a specialized GAN inversion method, based on an appropriate set of losses for learning an unmasking encoder. With extensive experiments, we show that the approach is effective at unmasking face images. In addition, we also show that the identity information is preserved sufficiently well to improve face verification performance based on several face recognition benchmark datasets.
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我们建议使用单个图像进行面部表达到表达翻译的简单而强大的地标引导的生成对抗网络(Landmarkgan),这在计算机视觉中是一项重要且具有挑战性的任务,因为表达到表达的翻译是非 - 线性和非对准问题。此外,由于图像中的对象可以具有任意的姿势,大小,位置,背景和自我观念,因此需要在输入图像和输出图像之间有一个高级的语义理解。为了解决这个问题,我们建议明确利用面部地标信息。由于这是一个具有挑战性的问题,我们将其分为两个子任务,(i)类别引导的地标生成,以及(ii)具有里程碑意义的指导表达式对表达的翻译。两项子任务以端到端的方式进行了培训,旨在享受产生的地标和表情的相互改善的好处。与当前的按键指导的方法相比,提议的Landmarkgan只需要单个面部图像即可产生各种表达式。四个公共数据集的广泛实验结果表明,与仅使用单个图像的最先进方法相比,所提出的Landmarkgan获得了更好的结果。该代码可从https://github.com/ha0tang/landmarkgan获得。
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变形攻击是一种表现攻击的一种形式,近年来引起了人们越来越多的关注。可以成功验证变形图像到多个身份。因此,此操作提出了与旅行或身份文件的能力有关的严重安全问题,该文件被证实属于多个人。以前的作品涉及了变形攻击图像质量的问题,但是,主要目标是定量证明产生的变形攻击的现实外观。我们认为,与真正的样品相比,变形过程可能会影响面部识别(FR)中的感知图像质量和图像实用程序。为了研究这一理论,这项工作对变形对面部图像质量的影响进行了广泛的分析,包括一般图像质量度量和面部图像实用程序测量。该分析不仅限于单个变形技术,而是使用十种不同的质量度量来研究六种不同的变形技术和五个不同的数据源。该分析揭示了变形攻击的质量得分与通过某些质量度量测量的真正样品的质量得分之间的一致性。我们的研究进一步建立在这种效果的基础上,并研究基于质量得分进行无监督的变形攻击检测(MAD)的可能性。我们的研究探索了intra和数据库间的可检测性,以评估这种检测概念在不同的变形技术和真正的源源源上的普遍性。我们的最终结果指出,一组质量措施(例如岩石和CNNIQA)可用于执行无监督和普遍的MAD,正确的分类精度超过70%。
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基于深卷积神经网络(CNN)的面部识别表现出归因于提取的高判别特征的卓越精度性能。然而,经常忽略了深度学习模型(深度特征)提取的功能的安全性和隐私。本文提出了从深度功能中重建面部图像,而无需访问CNN网络配置作为约束优化问题。这种优化可最大程度地减少从原始面部图像中提取的特征与重建的面部图像之间的距离。我们没有直接解决图像空间中的优化问题,而是通过寻找GAN发电机的潜在向量来重新重新制定问题,然后使用它来生成面部图像。 GAN发电机在这个新颖的框架中起着双重作用,即优化目标和面部发电机的面部分布约束。除了新颖的优化任务之外,我们还提出了一条攻击管道,以基于生成的面部图像模拟目标用户。我们的结果表明,生成的面部图像可以达到最先进的攻击率在LFW上的最先进的攻击率在I型攻击下为0.1 \%。我们的工作阐明了生物识别部署,以符合隐私和安全政策。
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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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横梁面部识别(CFR)旨在识别个体,其中比较面部图像源自不同的感测模式,例如红外与可见的。虽然CFR由于与模态差距相关的面部外观的显着变化,但CFR具有比经典的面部识别更具挑战性,但它在具有有限或挑战的照明的场景中,以及在呈现攻击的情况下,它是优越的。与卷积神经网络(CNNS)相关的人工智能最近的进展使CFR的显着性能提高了。由此激励,这项调查的贡献是三倍。我们提供CFR的概述,目标是通过首先正式化CFR然后呈现具体相关的应用来比较不同光谱中捕获的面部图像。其次,我们探索合适的谱带进行识别和讨论最近的CFR方法,重点放在神经网络上。特别是,我们提出了提取和比较异构特征以及数据集的重新访问技术。我们枚举不同光谱和相关算法的优势和局限性。最后,我们讨论了研究挑战和未来的研究线。
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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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The primary aim of single-image super-resolution is to construct a high-resolution (HR) image from a corresponding low-resolution (LR) input. In previous approaches, which have generally been supervised, the training objective typically measures a pixel-wise average distance between the super-resolved (SR) and HR images. Optimizing such metrics often leads to blurring, especially in high variance (detailed) regions. We propose an alternative formulation of the super-resolution problem based on creating realistic SR images that downscale correctly. We present a novel super-resolution algorithm addressing this problem, PULSE (Photo Upsampling via Latent Space Exploration), which generates high-resolution, realistic images at resolutions previously unseen in the literature. It accomplishes this in an entirely self-supervised fashion and is not confined to a specific degradation operator used during training, unlike previous methods (which require training on databases of LR-HR image pairs for supervised learning). Instead of starting with the LR image and slowly adding detail, PULSE traverses the high-resolution natural image manifold, searching for images that downscale to the original LR image. This is formalized through the "downscaling loss," which guides exploration through the latent space of a generative model. By leveraging properties of high-dimensional Gaussians, we restrict the search space to guarantee that our outputs are realistic. PULSE thereby generates super-resolved images that both are realistic and downscale correctly. We show extensive experimental results demonstrating the efficacy of our approach in the domain of face super-resolution (also known as face hallucination). We also present a discussion of the limitations and biases of the method as currently implemented with an accompanying model card with relevant metrics. Our method outperforms state-of-the-art methods in perceptual quality at higher resolutions and scale factors than previously pos-sible.
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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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近年来政府和商业实体的面部识别(FR)技术的快速采用提出了对公民自由和隐私的担忧。作为回应,已经开发了一套广泛的所谓“反面部识别”(AFR)工具,以帮助用户避免不需要的面部识别。在过去几年中提出的一组AFR工具是广泛的,快速发展,需要退回措施,以考虑AFR系统的更广泛的设计空间和长期挑战。本文旨在填补该差距,并提供对AFR研究景观的第一次综合分析。使用FR系统的运营级作为起点,我们创建了一个系统框架,用于分析不同AFR方法的益处和权衡。然后,我们考虑到AFR工具面临的技术和社会挑战,并提出在该领域的未来研究方向。
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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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在这项工作中,我们研究了面部重建的问题,鉴于从黑框面部识别引擎中提取的面部特征表示。确实,由于引擎中抽象信息的局限性,在实践中,这是非常具有挑战性的问题。因此,我们在蒸馏框架(dab-gan)中引入了一种名为基于注意力的生成对抗网络的新方法,以合成受试者的面孔,鉴于其提取的面部识别功能。鉴于主题的任何不受约束的面部特征,Dab-Gan可以在高清上重建他/她的脸。 DAB-GAN方法包括一种新型的基于注意力的生成结构,采用新的定义的Bioxtive Metrics学习方法。该框架首先引入徒图,以便可以在图像域中直接采用距离测量和度量学习过程,以进行图像重建任务。来自Blackbox面部识别引擎的信息将使用全局蒸馏过程最佳利用。然后,提出了一个基于注意力的发电机,以使一个高度可靠的发电机通过ID保存综合逼真的面孔。我们已经评估了有关具有挑战性的面部识别数据库的方法,即Celeba,LF​​W,AgeDB,CFP-FP,并始终取得了最新的结果。 Dab-Gan的进步也得到了图像现实主义和ID保存属性的证明。
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面部合成的进步已经提出了关于合成面的欺骗性使用的警报。合成综合性可以有效地用于欺骗人类观察者吗?在本文中,我们介绍了使用不同策略产生的合成面的人类感知的研究,包括基于最先进的深学的GaN模型。这是第一次严格研究从心理学的实验技术接地的合成面代发电技术的有效性研究。我们回答了重要的问题,如GaN的频率和更传统的图像处理的技术混淆人类观察者,并且在综合性脸部图像中有细微的线索,导致人类将其视为假冒,而无需寻找明显的线索还为了回答这些问题,我们进行了一系列大规模众群行为实验,具有不同的面膜。结果表明,人类无法在几个不同的情况下区分真实面的合成面。这一发现对面部图像呈现给人类用户的许多不同应用具有严重影响。
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Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. Some of them were applied in the industrial community and played an important role in human life such as device unlock, mobile payment, and so on. This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, mainstream training, evaluation datasets, and related applications. We have analyzed and compared state-of-the-art works as many as possible, and also carefully designed a set of experiments to find the effect of backbone size and data distribution. This survey is a material of the tutorial named The Practical Face Recognition Technology in the Industrial World in the FG2023.
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