一种基于特征正则约束的异常检测方法
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国家自然科学基金面上项目(61871278); 四川省科技厅国际科技合作与交流研发项目(2018HH0143); 成都市产业集群协同创新项目(2016-XT00-00015-GX)


An anomaly detection method based on feature regular constraints
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    摘要:

    异常检测是计算机视觉的一个经典问题.针对异常样本稀少在真实场景中异常很难被捕捉,且标签难以获取,提出一种仅用正常样本进行训练的端到端异常检测模型.首先,通过自动编码器对输入图像进行编码,得到它的低维特征;然后,用一个自回归概率密度估计器对低维特征的概率分布进行正则约束,解码器再将其恢复至原始输入大小;最后,使用一个分类器来判断生成图片的真假.编解码器之间使用了跳线连接,能够最大限度地提高该模型对正常样本的记忆能力.本文在CIFAR10和UCSD Ped2数据集上进行了实验,测试结果显示,IFAR10总共10个类别的平均曲线下面积(AUC)达到73.5%,UCSD Ped2的平均曲线下面积(AUC)达到95.7%.结果证明,该模型能够明显提高异常检测的效果.

    Abstract:

    Anomaly detection is a classic problem in computer vision. It is difficult to capture the anomalies in the real scene and is difficult to obtain the labels as well, an endtoend anomaly detection model trained only with normal samples is proposed. First, the input image is encoded by an automatic encoder to obtain its lowdimensional features, and then an autoregressive probability density estimator is used to constrain the probability distribution of low dimensional features. The decoder restores it to the original input size. Finally, a classifier determines the authenticity of the generated picture. A jumper connection is used between the codecs to maximize the memory of the model for normal samples. In this paper, the experiments were conducted on the CIFAR10 and UCSD Ped2 datasets. The results showed that the average area under the curve (AUC) of the 10 categories of CIFAR10 reached 73.5%, and the area under the average curve (AUC) of UCSDPed2 reached 95.7%. This model can effectively improve the effect of anomaly detection.

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引用本文格式: 邓 描,刘强,陈洪刚,王正勇,何小海. 一种基于特征正则约束的异常检测方法 [J]. 四川大学学报: 自然科学版, 2020, 57: 1077~1083.

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  • 收稿日期:2019-11-26
  • 最后修改日期:2020-04-27
  • 录用日期:2020-04-28
  • 在线发布日期: 2020-12-02