Videos represent the primary source of information for surveillance applications. Video material is often available in large quantities but in most cases it contains little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.
translated by 谷歌翻译
We propose a one-class neural network (OC-NN) model to detect anomalies in complex data sets. OC-NN combines the ability of deep networks to extract progressively rich representation of data with the one-class objective of creating a tight envelope around normal data. The OC-NN approach breaks new ground for the following crucial reason: data representation in the hidden layer is driven by the OC-NN objective and is thus customized for anomaly detection. This is a departure from other approaches which use a hybrid approach of learning deep features using an autoencoder and then feeding the features into a separate anomaly detection method like one-class SVM (OC-SVM). The hybrid OC-SVM approach is sub-optimal because it is unable to influence representational learning in the hidden layers. A comprehensive set of experiments demonstrate that on complex data sets (like CIFAR and GTSRB), OC-NN performs on par with state-of-the-art methods and outperformed conventional shallow methods in some scenarios.
translated by 谷歌翻译
使用降维的异常检测一直是监测多维数据的基本技术。虽然基于深度学习的方法因其卓越的检测性能而得到了很好的研究,但它们的可解释性仍然是个问题。在本文中,我们提出了一种用于通过使用变分自动编码器(VAE)来估计对检测到的异常有贡献的维度的算法。我们的算法基于一个考虑数据中存在异常的近似概率模型,并且通过最大化对数似然,我们估计哪些维度有助于将数据确定为异常。使用基准数据集的实验结果表明,我们的算法比基线方法更准确地提取贡献维度。
translated by 谷歌翻译
长期以来,一类支持向量机(OC-SVM)已成为最有效的异常检测方法之一,并在研究和工业应用中得到广泛应用。由于优化复杂性,OC-SVM的最大问题在于能够使用大型和高维数据集。可以通过诸如流形学习或自动编码器的维数减少技术来减轻这些问题。但是,以前的工作通常会分别处理代表性学习和异常预测。在本文中,我们提出了基于自动编码器的一类支持向量机(AE-1SVM),它将OC-SVM借助于随机傅立叶特征来近似径向基核,通过将其与表示学习架构相结合,并将其联合到深度学习环境中。利用随机梯度下降来获得端到端的训练。有趣的是,这也开辟了可能使用基于梯度的归因方法来解释异常检测的决策,这一直是挑战,因为输入空间和内核空间之间存在隐式映射。据我们所知,这一点是第一个研究异常检测中深度学习的解释性的工作。我们在广泛的无监督异常检测任务中评估我们的方法,其中我们的端到端训练架构比使用单独训练的先前工作实现了明显更好的性能。
translated by 谷歌翻译
异常检测是计算机视觉中的经典问题,即当数据集由于其他类别(异常)的样本量不足而高度偏向于一类(正常)时,异常的确定与异常的确定。虽然这可以作为监督学习问题来解决,但是一个更具挑战性的问题是检测未知/看不见的异常情况,这种情况将我们带入了一个半监督学习范式的空间。我们引入了这样一种新颖的异常检测模型,利用条件生成对抗网络,共同学习高维图像空间的生成和潜在空间的推理。在发电机网络中采用编码器 - 解码器 - 编码器子网络使得模型能够将输入图像映射到较低维度的向量,然后用于重建所生成的输出图像。附加编码器网络的使用将该生成的图像映射到其潜在的表示。在训练期间最小化这些图像与潜在向量之间的距离有助于学习正常样本的数据分布。结果,在推理时间从该学习的数据分布得到的较大距离度量指示该分布的异常值 - 异常。来自不同领域的几个基准数据集的实验显示了模型效率和优于先前技术方法的优势。
translated by 谷歌翻译
We propose an anomaly detection method using the reconstruction probability from the variational autoencoder. The reconstruction probability is a probabilistic measure that takes into account the variability of the distribution of variables. The reconstruction probability has a theoretical background making it a more principled and objective anomaly score than the reconstruction error, which is used by autoencoder and principal components based anomaly detection methods. Experimental results show that the proposed method outper-forms autoencoder based and principal components based methods. Utilizing the generative characteristics of the variational autoencoder enables deriving the reconstruction of the data to analyze the underlying cause of the anomaly.
translated by 谷歌翻译
新颖性检测是识别与训练集明显不同的数据异常的无监督问题。新颖性检测是机器学习中的经典挑战之一,也是欺诈检测,入侵检测,医疗诊断,数据清理和故障预防等几个研究领域的核心组成部分。虽然设计了许多算法来解决这个问题,但大多数方法仅适用于模拟连续数值数据。处理由混合类型特征(例如数值和分类数据)或描述离散事件序列的时间数据集组成的数据集是一项具有挑战性的任务。除了支持的数据类型之外,有效新颖性检测方法的关键标准是能够准确地将新颖性与标称样本分离,可解释性,可扩展性以及对位于训练数据中的异常的鲁棒性。在本文中,我们研究了解决这些问题的新方法。特别地,我们提出(i)混合型数据的新颖性检测方法的实验比较(ii)序列数据的新颖检测方法的实验比较,(iii)基于Dirichlet过程混合和指数的混合型数据的概率非参数奇异检测方法。 - 家庭分布和(iv)基于自动编码器的新奇检测模型,其编码器/解码器被建模为深度高斯过程。
translated by 谷歌翻译
We consider the problem of anomaly detection in images, and present a new detection technique. Given a sample of images, all known to belong to a "normal" class (e.g., dogs), we show how to train a deep neural model that can detect out-of-distribution images (i.e., non-dog objects). The main idea behind our scheme is to train a multi-class model to discriminate between dozens of geometric transformations applied on all the given images. The auxiliary expertise learned by the model generates feature detectors that effectively identify, at test time, anomalous images based on the softmax activation statistics of the model when applied on transformed images. We present extensive experiments using the proposed detector, which indicate that our technique consistently improves all known algorithms by a wide margin.
translated by 谷歌翻译
无线传感器网络(WSN)通过弥合物理和网络世界之间的差距,是物联网(IoT)的基础。在这种情况下,异常检测是一项关键任务,因为它负责识别各种利益事件,例如设备故障和未发现的现象。然而,由于异常的特性和周围环境的波动性,这项任务具有挑战性。在像WSN这样资源稀缺的环境中,这一挑战进一步提升,并削弱了许多现有解决方案的适用性。本文首次将自动编码器神经网络引入到WSN中,解决了异常检测问题。我们设计了一个分为传感器和物联网云的两部分算法,这样(i)可以以完全分布的方式在传感器中检测到异常,而无需与任何其他传感器或云通信,以及(ii)相对更多的计算密集学习任务可由云以更低(和可配置)的频率处理。除了最小的通信开销之外,传感器上的计算负荷也非常低(多项式复杂性),并且大多数COTS传感器都能轻松承受。使用连续4个月收集的真实WSNindoor测试平台和传感器数据,我们通过实验证明我们提出的基于自动编码器的异常检测机制可以实现高检测精度和低误报率。它还能够适应非预测的和新的非变化由于我们选择的自编码神经网络的无监督学习功能,静止环境。
translated by 谷歌翻译
异常检测通常被认为是机器学习的一个具有挑战性的领域,因为难以获得用于训练的异常样本并且需要获得足够量的训练数据。近年来,自动编码器已被证明是仅对“正常”数据进行训练的有效异常检测器。生成对抗网络(GAN)已被用于为分类器生成附加训练样本,从而使它们更准确和更强。然而,在异常检测中,GAN仅用于重建样本而不是用于生成其他样本。这源于大多数领域中少量和缺乏异常数据的多样性。在这项研究中,我们提出了MDGAN,一种新颖的GAN架构,用于通过生成其他样品来改善异常检测。我们的方法使用双歧视器:用于确定生成的样本是否具有足够质量(即有效)的密集网络和用作异常检测器的自动编码器。 MDGAN使我们能够协调两个相互冲突的目标:1)生成可以欺骗第一个鉴别器的高质量样本,以及2)生成最终可由第二个鉴别器重建的样本,从而提高其性能。对adiverse数据集的实证评估证明了我们的方法的优点。
translated by 谷歌翻译
This paper proposes to use autoencoders with nonlinear di-mensionality reduction in the anomaly detection task. The authors apply dimensionality reduction by using an autoen-coder onto both artificial data and real data, and compare it with linear PCA and kernel PCA to clarify its property. The artificial data is generated from Lorenz system, and the real data is the spacecrafts' telemetry data. This paper demonstrates that autoencoders are able to detect subtle anomalies which linear PCA fails. Also, autoencoders can increase their accuracy by extending them to denoising au-toenconders. Moreover, autoencoders can be useful as non-linear techniques without complex computation as kernel PCA requires. Finaly, the authors examine the learned features in the hidden layer of autoencoders, and present that autoencoders learn the normal state properly and activate differently with anomalous input.
translated by 谷歌翻译
本文提出了一种新的优化原理及其在声音(ADS)中使用自动编码器(AE)进行无监督异常检测的实现。无监督ADS的目的是在没有异常声音训练数据的情况下检测未知的异常声音。使用AE作为正常模型是无监督ADS的最先进技术。为了降低误报率(FPR),训练AE以最小化正常声音的重建误差,并且将异常分数计算为观察到的声音的重建误差。不幸的是,由于该训练过程没有考虑异常的异常分数。听起来,真正的阳性率(TPR)并没有必然增加。在本研究中,我们通过将ADS作为统计假设检验来定义基于Neyman-Pearson引理的目标函数。所提出的目标函数训练AE以在任意低FPR条件下最大化TPR。为了计算目标函数中的TPR,我们考虑一组异常声音是正常声音的互补集合,并使用拒绝抽样算法模拟异常声音。通过使用合成数据的实验,我们发现提出的方法改进了ADS的性能指标。在低FPR条件下。此外,我们确认所提出的方法可以检测重新环境中的异常声音。
translated by 谷歌翻译
异常检测是一个重要且因此充分研究的问题。然而,开发复杂和高维数据的有效异常检测方法仍然是一个挑战。由于生成性对抗网络(GAN)能够模拟现实世界数据的复杂高维分布,因此它们不具备应对这一挑战的有希望的方法。在这项工作中,我们提出了一种异常检测方法,即基于双向GAN的异常学习异常检测(ALAD),它为异常检测任务提供了对抗性学习的特征。然后,ALAD使用基于这些对等学习特征的重建错误来确定数据样本是否异常。 ALAD建立了最近的进展,以确保数据空间和潜在的太空周期一致性并稳定GAN培训,从而显着提高异常检测性能。 ALAD在一系列图像和表格数据集上实现了最先进的性能,同时在测试时间比仅发布的基于GAN的方法快几百倍。
translated by 谷歌翻译
As advances in networking technology help to connect the distant corners of the globe and as the Internet continues to expand its influence as a medium for communications and commerce, the threat from spammers, attackers and criminal enterprises has also grown accordingly. It is the prevalence of such threats that has made intrusion detection systems-the cyberspace's equivalent to the burglar alarm-join ranks with firewalls as one of the fundamental technologies for network security. However, today's commercially available intrusion detection systems are predominantly signature-based intrusion detection systems that are designed to detect known attacks by utilizing the signatures of those attacks. Such systems require frequent rule-base updates and signature updates, and are not capable of detecting unknown attacks. In contrast, anomaly detection systems, a subset of intrusion detection systems, model the normal system/network behavior which enables them to be extremely effective in finding and foiling both known as well as unknown or ''zero day'' attacks. While anomaly detection systems are attractive conceptually, a host of technological problems need to be overcome before they can be widely adopted. These problems include: high false alarm rate, failure to scale to gigabit speeds, etc. In this paper, we provide a comprehensive survey of anomaly detection systems and hybrid intrusion detection systems of the recent past and present. We also discuss recent technological trends in anomaly detection and identify open problems and challenges in this area.
translated by 谷歌翻译
人类堕落很少发生;然而,从健康和安全的角度来看,检测跌倒是非常重要的。由于跌倒的罕见性,很难采用监督分类技术来检测它们。此外,在这些高度偏斜的情况下,还难以提取域特定特征以识别跌倒。在本文中,我们提出了一个新的框架,\ textit {DeepFall},它将跌倒检测问题表述为异常检测问题。 \ textit {DeepFall}框架提供了深度时空卷积自动编码器的新颖用途,以使用非侵入式感知模式学习正常活动的空间和时间特征。我们还提出了一种新的异常评分方法,该方法将视频序列中的重建分数组合结合起来,以检测看不见的跌倒。我们在通过非侵入式感应模式,热像仪和深度相机收集的三个公开可用数据集上测试了\ textit {DeepFall}框架,并且与传统的自动编码器和卷积自动编码器方法相比,显示出优异的结果,以识别看不见的跌倒。
translated by 谷歌翻译
Unsupervised anomaly detection on multi-or high-dimensional data is of great importance in both fundamental machine learning research and industrial applications , for which density estimation lies at the core. Although previous approaches based on dimensionality reduction followed by density estimation have made fruitful progress, they mainly suffer from decoupled model learning with inconsistent optimization goals and incapability of preserving essential information in the low-dimensional space. In this paper, we present a Deep Autoencoding Gaussian Mixture Model (DAGMM) for unsupervised anomaly detection. Our model utilizes a deep autoencoder to generate a low-dimensional representation and reconstruction error for each input data point, which is further fed into a Gaussian Mixture Model (GMM). Instead of using decoupled two-stage training and the standard Expectation-Maximization (EM) algorithm, DAGMM jointly optimizes the parameters of the deep autoencoder and the mixture model simultaneously in an end-to-end fashion, leveraging a separate estimation network to facilitate the parameter learning of the mixture model. The joint optimization, which well balances autoencoding reconstruction, density estimation of latent representation, and regularization, helps the autoencoder escape from less attractive local optima and further reduce reconstruction errors, avoiding the need of pre-training. Experimental results on several public benchmark datasets show that, DAGMM significantly outperforms state-of-the-art anomaly detection techniques, and achieves up to 14% improvement based on the standard F 1 score.
translated by 谷歌翻译
Surveillance videos are able to capture a variety of realistic anomalies. In this paper, we propose to learn anomalies by exploiting both normal and anomalous videos. To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i.e. the training labels (anomalous or normal) are at video-level instead of clip-level. In our approach, we consider normal and anomalous videos as bags and video segments as instances in multiple instance learning (MIL), and automatically learn a deep anomaly ranking model that predicts high anomaly scores for anomalous video segments. Furthermore , we introduce sparsity and temporal smoothness constraints in the ranking loss function to better localize anomaly during training. We also introduce a new large-scale first of its kind dataset of 128 hours of videos. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies such as fighting, road accident, burglary, robbery , etc. as well as normal activities. This dataset can be used for two tasks. First, general anomaly detection considering all anomalies in one group and all normal activities in another group. Second, for recognizing each of 13 anomalous activities. Our experimental results show that our MIL method for anomaly detection achieves significant improvement on anomaly detection performance as compared to the state-of-the-art approaches. We provide the results of several recent deep learning baselines on anomalous activity recognition. The low recognition performance of these baselines reveals that our dataset is very challenging and opens more opportunities for future work. The dataset is available at: http://crcv.ucf.edu/projects/real-world/
translated by 谷歌翻译
We present a novel unsupervised deep learning framework for anomalous event detection in complex video scenes. While most existing works merely use hand-crafted appearance and motion features, we propose Appearance and Motion DeepNet (AMDN) which utilizes deep neural networks to automatically learn feature representations. To exploit the complementary information of both appearance and motion patterns, we introduce a novel double fusion framework, combining both the benefits of traditional early fusion and late fusion strategies. Specifically, stacked denoising autoencoders are proposed to separately learn both appearance and motion features as well as a joint representation (early fusion). Based on the learned representations, multiple one-class SVM models are used to predict the anomaly scores of each input, which are then integrated with a late fusion strategy for final anomaly detection. We evaluate the proposed method on two publicly available video surveillance datasets, showing competitive performance with respect to state of the art approaches.
translated by 谷歌翻译
最近,生成对抗网络(GAN)和变体的引入使得能够生成逼真的合成样本,其已被用于扩大训练集。以前的工作主要集中在半监督和监督任务的数据增强。在本文中,我们主要关注无监督异常检测,并提出了一种新的生成数据增强框架,该框架针对此任务进行了优化。特别是,我们建议对不频繁的正常样本进行过采样 - 正常样本以较小的概率发生,例如罕见的正常事件。我们表明这些样本对异常检测中的假阳性负责。然而,对于具有多峰分布的现实世界的高维数据,对频繁的正常样本的过采样是具有挑战性的。为了解决这一挑战,我们建议使用称为对抗性自动编码器(AAE)的aGAN变体将高维多模态数据分布转换为具有明确定义的尾概率的低维单向分布。然后,在潜在分布的“边缘”进行系统过采样,以增加不频繁的正常样本的密度。我们表明我们的过采样管道是统一的:它通常适用于具有不同复杂数据分布的数据集。据我们所知,我们的方法是第一个专注于提高不受监督的异常检测性能的数据增强技术。我们通过展示几个真实数据集的一致性改进来验证我们的方法。
translated by 谷歌翻译