Anomalous behavior detection is a challenging research area within computer
vision. Progress in this area enables automated detection of dangerous behavior
using surveillance camera feeds. A dangerous behavior that is often overlooked
in other research is the throwing action in traffic flow, which is one of the
unique requirements of our Smart City project to enhance public safety. This
paper proposes a solution for throwing action detection in surveillance videos
using deep learning. At present, datasets for throwing actions are not publicly
available. To address the use-case of our Smart City project, we first generate
the novel public 'Throwing Action' dataset, consisting of 271 videos of
throwing actions performed by traffic participants, such as pedestrians,
bicyclists, and car drivers, and 130 normal videos without throwing actions.
Second, we compare the performance of different feature extractors for our
anomaly detection method on the UCF-Crime and Throwing-Action datasets. The
explored feature extractors are the Convolutional 3D (C3D) network, the
Inflated 3D ConvNet (I3D) network, and the Multi-Fiber Network (MFNet).
Finally, the performance of the anomaly detection algorithm is improved by
applying the Adam optimizer instead of Adadelta, and proposing a mean normal
loss function that covers the multitude of normal situations in traffic. Both
aspects yield better anomaly detection performance. Besides this, the proposed
mean normal loss function lowers the false alarm rate on the combined dataset.
The experimental results reach an area under the ROC curve of 86.10 for the
Throwing-Action dataset, and 80.13 on the combined dataset, respectively.

该研究论文提出了一个基于深度学习的解决方案，用于监控视频中的投掷行为检测，并通过生成新的公开数据集和比较不同特征提取器的性能来改善异常检测算法，实验结果显示了良好的性能。

监控视频中的物体投掷行为检测

Detection of Object Throwing Behavior in Surveillance Videos

In vertical federated learning (VFL), commercial entities collaboratively
train a model while preserving data privacy. However, a malicious participant's
poisoning attack may degrade the performance of this collaborative model. The
main challenge in achieving the poisoning attack is the absence of access to
the server-side top model, leaving the malicious participant without a clear
target model. To address this challenge, we introduce an innovative end-to-end
poisoning framework P-GAN. Specifically, the malicious participant initially
employs semi-supervised learning to train a surrogate target model.
Subsequently, this participant employs a GAN-based method to produce
adversarial perturbations to degrade the surrogate target model's performance.
Finally, the generator is obtained and tailored for VFL poisoning. Besides, we
develop an anomaly detection algorithm based on a deep auto-encoder (DAE),
offering a robust defense mechanism to VFL scenarios. Through extensive
experiments, we evaluate the efficacy of P-GAN and DAE, and further analyze the
factors that influence their performance.

在垂直联邦学习中，通过保护数据隐私，商业实体共同训练一个模型。然而，恶意参与者的毒化攻击可能会降低这个合作模型的性能。为了解决这个挑战，本文引入了一种创新的端到端毒化框架 P-GAN。同时，我们还开发了一种基于深度自编码器（DAE）的异常检测算法，为垂直联邦学习情景提供了强大的防御机制。通过大量实验，我们评估了 P-GAN 和 DAE 的效能，并进一步分析了影响它们性能的因素。

基于 GAN 的数据污染框架用于对抗垂直联邦学习中的异常检测

A GAN-based data poisoning framework against anomaly detection in  vertical federated learning

This paper employs an anomaly detection algorithm to assess the normal
operation of underwater gliders in unpredictable ocean environments. Real-time
alerts can be provided to glider pilots upon detecting any anomalies, enabling
them to assume control of the glider and prevent further harm. The detection
algorithm is applied to abundant data sets collected in real glider deployments
led by the Skidaway Institute of Oceanography (SkIO) and the University of
South Florida (USF). Regarding generality, the experimental evaluation is
composed of both offline and online detection modes. The offline detection
utilizes full post-recovery data sets, which carries high-resolution
information, to present detailed analysis of the anomaly and compare it with
pilot logs. The online detection focuses on the real-time subsets of data
transmitted from the glider at the surfacing events. While the real-time data
may not contain as much rich information as the post-recovery data, the online
detection is of great importance as it allows glider pilots to monitor
potential abnormal conditions in real time.

该论文使用异常检测算法评估水下滑翔器在不可预测的海洋环境中的正常操作，并在检测到任何异常时向滑翔器飞行员提供实时警报，使其能够控制滑翔器并防止进一步损害。异常检测算法应用于由 Skidaway 海洋学研究所（SkIO）和南佛罗里达大学（USF）领导的实际滑翔器部署所收集到的丰富数据集。实验评估包括离线和在线检测模式。离线检测利用完整的恢复后数据集进行，提供异常的详细分析，并与飞行员日志进行比较。在线检测则专注于滑翔器在上浮事件时传输的实时数据子集。尽管实时数据可能不含有恢复后数据那么丰富的信息，但在线检测非常重要，因为它允许滑翔器飞行员实时监测潜在的异常条件。