面部表现攻击检测（PAD）对于保护面部识别（FR）应用程序至关重要。 FR性能已被证明对某些人口统计学和非人口统计学组是不公平的。但是，面部垫的公平性是一个研究的问题，这主要是由于缺乏适当的注释数据。为了解决此问题，这项工作首先通过组合几个知名的PAD数据集，在其中提供了七个人类宣传的属性标签，从而提出了一个组合的注释数据集（CAAD-PAD）。然后，这项工作通过研究我们的CAAD-Pad上的四个面部垫方法，全面分析了一组面垫的公平及其与培训数据的性质和操作决策阈值分配（ODTA）的关系。同时代表垫子的公平性和绝对垫性能，我们引入了一种新颖的指标，即准确性平衡公平（ABF）。关于CAAD-PAD的广泛实验表明，训练数据和ODTA会引起性别，遮挡和其他属性组的不公平性。基于这些分析，我们提出了一种数据增强方法Fairswap，该方法旨在破坏身份/语义信息和指南模型以挖掘攻击线索而不是与属性相关的信息。详细的实验结果表明，Fairswap通常可以提高垫子性能和面部垫的公平性。

基于监督的基于学习的形态攻击检测（MAD）解决方案在处理已知变形技术和已知数据源的攻击方面取得了杰出的成功。但是，鉴于变形攻击的变化，由于现有MAD数据集的多样性和数量不足，监督的疯狂解决方案的性能大大下降。为了解决这一问题，我们通过利用现有的大规模面部识别（FR）数据集和卷积自动编码器的无监督性质，通过自定进程异常检测（SPL-MAD）提出了一个完全无监督的疯狂解决方案。使用一般的FR数据集，这些数据集可能包含无意识的和未标记的操纵样品来训练自动编码器，可以导致攻击和真正的样本的各种重建行为。我们从经验上分析了这种行为，以提供扎实的理论基础来设计我们的无监督的疯狂解决方案。这也导致建议以完全无监督的方式整合我们改良的修改后的自定进度学习范式，以增强善意和攻击样本之间的重建误差可分离性。我们对各种MAD评估数据集的实验结果表明，所提出的无监督的SPL-MAD解决方案优于广泛监督的MAD解决方案的整体性能，并为未知攻击提供了更高的概括性。

面部图像的质量显着影响底层识别算法的性能。面部图像质量评估（FIQA）估计捕获的图像的效用在实现可靠和准确的识别性能方面。在这项工作中，我们提出了一种新的学习范式，可以在培训过程中学习内部网络观察。基于此，我们所提出的CR-FiQA使用该范例来通过预测其相对分类性来估计样品的面部图像质量。基于关于其类中心和最近的负类中心的角度空间中的训练样本特征表示来测量该分类性。我们通过实验说明了面部图像质量与样本相对分类性之间的相关性。由于此类属性仅为培训数据集可观察到，因此我们建议从培训数据集中学习此属性，并利用它来预测看不见样品的质量措施。该培训同时执行，同时通过用于面部识别模型训练的角度裕度罚款的软墨损失来优化类中心。通过对八个基准和四个面部识别模型的广泛评估实验，我们展示了我们提出的CR-FiQA在最先进（SOTA）FIQ算法上的优越性。

戴着面具已被证明是防止SARS-COV-2冠状病毒传播最有效的方法之一。然而，佩戴掩模对不同的面部识别任务构成挑战，并提高了关于掩蔽面部呈现检测（焊盘）的性能的担忧。面向面膜面板面临的主要问题是错误分类的Bona Fide掩盖面，错误分类的部分攻击（由真实面具覆盖）。这项工作通过提出考虑部分攻击标签来监督垫模型培训的方法，以及区域加权推理，通过改变对不同面部区域的关注来进一步改善垫性能的方法来解决这些问题。我们所提出的方法与特定网络架构没有直接链接，因此可以直接纳入任何常见或定制设计的网络。在我们的工作中，选择了两个神经网络（DeepPixbis和MixfaceNet）作为骨干。在协作实际掩模攻击（CRMA）数据库上证明了实验。我们所提出的方法通过减少面向遮阳面时的缺点来优于CRMA数据库中的建立的焊盘方法。此外，我们提出了一个详细的逐步消融研究，指出了所提出的概念对整体垫性能的个人和联合益处。

随着在我们日常生活中的面部识别系统的部署增加，面部呈现攻击检测（PAD）在保护面部识别系统中吸引了很多关注并发挥着关键作用。尽管通过在数据集中的手工制作和基于深度学习的方法方面取得了巨大表现，但在处理看不见场景时的性能下降。在这项工作中，我们提出了一种双流卷积神经网络（CNNS）框架。一个流适应四种学习频率滤波器，以学习频域中的特征，这些功能域不太受传感器/照明的变化的影响。另一个流利用RGB图像来补充频域的特征。此外，我们提出了分层关注模块集成，通过考虑CNN的不同层中的深度特征的性质，在不同阶段中加入来自两个流的信息。在数据集内和交叉数据集设置中评估所提出的方法，结果表明，我们所提出的方法在与最先进的最先进的最新的大多数实验装置中提高了最平移，包括明确为域适应设计的方法/换档问题。我们成功证明了我们提出的垫解决方案的设计，在一步的融合研究中，涉及我们所提出的学习频率分解，我们的分层注意模块设计和使用的损耗功能。培训码和预先接受训练的型号是公开发布的

面部面罩已成为减少Covid-19传输的主要方法之一。这使得面部识别（FR）成为一个具有挑战性的任务，因为掩模隐藏了几个面孔的鉴别特征。此外，面部呈现攻击检测（PAD）至关重要，以确保FR系统的安全性。与越来越多的蒙面的FR研究相比，尚未探索面部遮蔽攻击对垫的影响。因此，我们提出了与戴上面具的主题和攻击的真正面罩的新型攻击，以反映当前的现实情况。此外，本研究通过在不同的实验设置下使用七种最新的垫算法来研究屏蔽攻击对垫性能的影响。我们还评估FR系统漏洞屏蔽攻击。实验表明，真正掩盖的攻击对FR系统的操作和安全构成了严重威胁。

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

Increasing research interests focus on sequential recommender systems, aiming to model dynamic sequence representation precisely. However, the most commonly used loss function in state-of-the-art sequential recommendation models has essential limitations. To name a few, Bayesian Personalized Ranking (BPR) loss suffers the vanishing gradient problem from numerous negative sampling and predictionbiases; Binary Cross-Entropy (BCE) loss subjects to negative sampling numbers, thereby it is likely to ignore valuable negative examples and reduce the training efficiency; Cross-Entropy (CE) loss only focuses on the last timestamp of the training sequence, which causes low utilization of sequence information and results in inferior user sequence representation. To avoid these limitations, in this paper, we propose to calculate Cumulative Cross-Entropy (CCE) loss over the sequence. CCE is simple and direct, which enjoys the virtues of painless deployment, no negative sampling, and effective and efficient training. We conduct extensive experiments on five benchmark datasets to demonstrate the effectiveness and efficiency of CCE. The results show that employing CCE loss on three state-of-the-art models GRU4Rec, SASRec, and S3-Rec can reach 125.63%, 69.90%, and 33.24% average improvement of full ranking NDCG@5, respectively. Using CCE, the performance curve of the models on the test data increases rapidly with the wall clock time, and is superior to that of other loss functions in almost the whole process of model training.

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.