深度神经网络（DNNS）的广泛应用要求越来越多的关注对其现实世界的鲁棒性，即DNN是否抵抗黑盒对抗性攻击，其中包括基于得分的查询攻击（SQA）是最威胁性的。由于它们的实用性和有效性：攻击者只需要在模型输出上进行数十个查询即可严重伤害受害者网络。针对SQA的防御需要对用户的服务目的而略有但巧妙的输出变化，这些用户与攻击者共享相同的输出信息。在本文中，我们提出了一种称为统一梯度（UNIG）的现实世界防御，以统一不同数据的梯度，以便攻击者只能探究不同样本相似的较弱的攻击方向。由于这种普遍的攻击扰动的验证与投入特定的扰动相比，Unig通过指示攻击者一个扭曲且信息不足的攻击方向来保护现实世界中的DNN。为了增强Unig在现实世界应用中的实际意义，我们将其实现为Hadamard产品模块，该模块具有计算效率且很容易插入任何模型。根据对5个SQA和4个防御基线的广泛实验，Unig显着改善了现实世界的鲁棒性，而不会伤害CIFAR10和Imagenet上的清洁准确性。例如，Unig在2500 Query Square攻击下保持了77.80％精度的CIFAR-10模型，而最先进的对手训练的模型仅在CIFAR10上具有67.34％的速度。同时，Unig在清洁精度和输出的修改程度上大大超过了所有基准。代码将发布。

The score-based query attacks (SQAs) pose practical threats to deep neural networks by crafting adversarial perturbations within dozens of queries, only using the model's output scores. Nonetheless, we note that if the loss trend of the outputs is slightly perturbed, SQAs could be easily misled and thereby become much less effective. Following this idea, we propose a novel defense, namely Adversarial Attack on Attackers (AAA), to confound SQAs towards incorrect attack directions by slightly modifying the output logits. In this way, (1) SQAs are prevented regardless of the model's worst-case robustness; (2) the original model predictions are hardly changed, i.e., no degradation on clean accuracy; (3) the calibration of confidence scores can be improved simultaneously. Extensive experiments are provided to verify the above advantages. For example, by setting $\ell_\infty=8/255$ on CIFAR-10, our proposed AAA helps WideResNet-28 secure 80.59% accuracy under Square attack (2500 queries), while the best prior defense (i.e., adversarial training) only attains 67.44%. Since AAA attacks SQA's general greedy strategy, such advantages of AAA over 8 defenses can be consistently observed on 8 CIFAR-10/ImageNet models under 6 SQAs, using different attack targets, bounds, norms, losses, and strategies. Moreover, AAA calibrates better without hurting the accuracy. Our code is available at https://github.com/Sizhe-Chen/AAA.

单步逆势培训（AT）受到了广泛的关注，因为它被证明是有效和健壮的。然而，存在严重的灾难性过度问题，即反对投影梯度下降（PGD）攻击的强劲准确性突然下降到培训期间的0.5美元。在本文中，我们从优化的新角度来看，首先揭示每个样品和过度装箱的快速增长梯度之间的密切联系，这也可以应用于了解多步骤中的稳健的过度拟合现象。为了控制培训期间梯度的增长，我们提出了一种新的方法，子空间对抗训练（子AT），限制了仔细提取的子空间。它成功地解决了两种过度装备，因此显着提高了鲁棒性。在子空间中，我们还允许单步合并较大的步骤和更大的半径，从而进一步提高了鲁棒性性能。因此，我们实现了最先进的单步性能：我们的纯单步可以达到超过$ \ mathbf {51} \％$鲁棒准确性，反对强大的PGD-50攻击以半径8美元/ CiFar-10上的255美元，甚至超过了标准的多步PGD-10，具有巨大的计算优势。代码已释放$ \脚注{\ url {https://github.com/nblt/sub -at}} $。

深度神经网络（DNN）被视为易受对抗性攻击的影响，而现有的黑匣子攻击需要广泛查询受害者DNN以实现高成功率。对于查询效率，由于它们的梯度相似度（GS），即代理的攻击梯度与受害者的攻击梯度类似，因此使用受害者的代理模型来生成可转移的对抗性示例（AES）。但是，通常忽略了它们对输出的相似性，即预测相似性（PS），以在不查询受害者的情况下通过代理过滤效率低效查询。要共同利用和还优化代理者的GS和PS，我们开发QueryNet，一个可以显着减少查询的统一攻击框架。 Querynet通过多识别代理人创造性地攻击，即通过不同的代理商为一个样本工艺几个AES，并且还使用代理人来决定查询最有前途的AE。之后，受害者的查询反馈累积以优化代理人的参数，还可以优化其架构，增强GS和PS。虽然Querynet无法获得预先接受预先训练的代理人，但根据我们的综合实验，它与可接受的时间内的替代方案相比，它会降低查询。 ImageNet，只允许8位图像查询，无法访问受害者的培训数据。代码可在https://github.com/allenchen1998/querynet上获得。

本文侧重于对探测器的高可转移的对抗性攻击，这很难以黑盒方式攻击，因为它们的多重输出特征和跨架构的多样性。为了追求高攻击可转让性，一种合理的方式是在探测器中找到一个共同的财产，这促进了常见弱点的发现。我们是第一个建议，来自探测器的解释器的相关性图是这样的财产。基于它，我们设计了对探测器（RAD）的相关性攻击，这实现了最先进的可转移性，超过了现有的结果超过20％。在MS Coco上，所有8个黑匣子架构的检测映射大于减半，并且分割地图也受到显着影响。鉴于RAD的巨大可转换性，我们生成用于对象检测和实例分割的第一个对抗性数据集，即对上下文（AOCO）的对手对象，这有助于快速评估和改进探测器的稳健性。

在视觉和声音内利用时间同步和关联是朝向探测物体的强大定位的重要一步。为此，我们提出了一个节省空间内存网络，用于探测视频中的对象本地化。它可以同时通过音频和视觉方式的单模和跨模型表示来同时学习时空关注。我们在定量和定性地展示和分析了在本地化视听物体中结合时空学习的有效性。我们展示了我们的方法通过各种复杂的视听场景概括，最近最先进的方法概括。

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

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.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.