深度学习在各种工业应用中取得了巨大成功。公司不希望他们的宝贵数据被恶意员工偷来培训盗版模式。他们也不希望竞争对手在线使用后分析的数据。我们提出了一种新的解决方案，在这种情况下，通过稳健地并可逆地将图像转换为对手图像。我们开发一个可逆的对抗性示例生成器（Raeg），对图像引入略微变化以欺骗传统的分类模型。尽管恶意攻击培训基于Deacened版本的受保护图像的盗版模型，但Raeg可以显着削弱这些模型的功能。同时，Raeg的可逆性确保了授权模型的表现。广泛的实验表明，Raeg可以通过比以前的方法更好地防止对抗对抗防御的轻微扭曲。

人类或语言模型创建的文本内容通常被对手被盗或滥用。跟踪文本出处可以帮助索取文本内容的所有权，或者标识分发误导内容的恶意用户，如机器生成的假新闻。有一些尝试实现这一目标，主要基于水印技术。具体而言，传统文本水印方法通过略微改变文本格式，如线间距和字体略微改变，但是，这是易碎的跨媒体传输，如OCR。考虑到这一点，自然语言水印方法通过用手工杂志资源（例如Wordnet）的同义词替换原始句子中的单词来代表水印，但他们不考虑替换对整体句子的意义的影响。最近，提出了一种基于变换器的网络来通过修改不引人注意的单词（例如，功能词）来嵌入水印，这也损害了句子的逻辑和语义连贯性。此外，一个训练有素的网络在其他不同类型的文本内容上都会失败。为了解决上述限制，我们提出了一种基于背景感知词汇替代（LS）的自然语言水印方案。具体而言，我们使用BERT来推断候选人与原句与原始句子之间的语义相关性建议LS候选。基于此，进一步设计了在同步性和替代性方面的选择策略，以测试一个单词是否完全适合于携带水印信号。广泛的实验表明，在客观和主观度量下，我们的水印方案可以很好地保持原始句子的语义完整性，并且具有比现有方法更好的可转换性。此外，拟议的LS方法优于斯坦福词语替代基准测试的最先进的方法。

Open software supply chain attacks, once successful, can exact heavy costs in mission-critical applications. As open-source ecosystems for deep learning flourish and become increasingly universal, they present attackers previously unexplored avenues to code-inject malicious backdoors in deep neural network models. This paper proposes Flareon, a small, stealthy, seemingly harmless code modification that specifically targets the data augmentation pipeline with motion-based triggers. Flareon neither alters ground-truth labels, nor modifies the training loss objective, nor does it assume prior knowledge of the victim model architecture, training data, and training hyperparameters. Yet, it has a surprisingly large ramification on training -- models trained under Flareon learn powerful target-conditional (or "any2any") backdoors. The resulting models can exhibit high attack success rates for any target choices and better clean accuracies than backdoor attacks that not only seize greater control, but also assume more restrictive attack capabilities. We also demonstrate the effectiveness of Flareon against recent defenses. Flareon is fully open-source and available online to the deep learning community: https://github.com/lafeat/flareon.

Edge-assisted vehicle-to-everything (V2X) motion planning is an emerging paradigm to achieve safe and efficient autonomous driving, since it leverages the global position information shared among multiple vehicles. However, due to the imperfect channel state information (CSI), the position information of vehicles may become outdated and inaccurate. Conventional methods ignoring the communication delays could severely jeopardize driving safety. To fill this gap, this paper proposes a robust V2X motion planning policy that adapts between competitive driving under a low communication delay and conservative driving under a high communication delay, and guarantees small communication delays at key waypoints via power control. This is achieved by integrating the vehicle mobility and communication delay models and solving a joint design of motion planning and power control problem via the block coordinate descent framework. Simulation results show that the proposed driving policy achieves the smallest collision ratio compared with other benchmark policies.

集成感应和通信（ISAC）代表范式转移，以前竞争的无线传输是共同设计的，可通过共同使用硬件平台来提高光谱，能源和硬件效率来和谐地运行。但是，由于诸如褪色和堵塞之类的对抗性因素，ISAC无融合可能会遭受高感知不确定性的影响。本文提出了一个多点ISAC（MPISAC）系统，该系统通过利用多雷达数据冗余来融合来自多个ISAC设备的输出，以实现更高的感应性能。此外，我们建议通过功能选择模块有效地探索传感和通信之间的性能权衡，该功能选择模块可适应地确定ISAC设备的工作状态（即传感或通信）。我们方法的症结在于采用融合模型，该模型通过假设检验和最佳投票分析来预测融合精度。仿真结果表明，MPISAC优于各种基准方案，并表明所提出的方法可以有效地跨越ISAC系统中的权衡区域。

准确预测短期OD矩阵（即，从各种来源到目的地的乘客流量的分布）是地铁系统中的一个重要任务。由于许多影响因素的不断变化的性质和实时延迟数据收集问题，这是强大的挑战性。最近，已经提出了一些基于学习的基于学习的模型，以便在乘车和高速公路中进行OD矩阵预测。然而，由于其不同的先验知识和上下文设置，这些模型不能充分捕获地铁网络中的站点之间的复杂时空相关性。在本文中，我们提出了一个混合框架多视图Trgru来解决OD Metro Matrix预测。特别是，它使用三个模块来模拟三个流动变化模式：最近的趋势，日常趋势，每周趋势。在每个模块中，基于每个站的嵌入的多视图表示被构造并馈送到基于变压器的门控复发结构，以通过全球自我注意机制捕获不同站的OD流的动态空间依赖性。在三种大型现实世界地铁数据集上进行了广泛的实验，证明了我们的多视图Trgru在其他竞争对手的优越性。

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.