Anomaly detection is an important task in many real-world applications, such as within cybersecurity and surveillance. As with most data these days, the size and dimensionality of the data within these fields are constantly growing, which makes it essential to develop an approach that can both accurately and efficiently identify anomalies within these datasets. In this paper, we address the problem of one-class anomaly detection, where after training on a singular class, we try to determine whether or not inputs belong to that said class. Most of the currently existing methods have limitations in which the criterion of the novel class relies solely on the reconstruction error term. We attempt to break away from this restriction by proposing the use of an autoencoder network with a normalization term. We pair this with an additive novelty scoring module during the training procedure as a way to determine the difference between a given image and our determined normal class, therefore improving the efficiency of our model. We evaluate our model on MNIST, CIFAR-10, and Fashion-MNIST, three popular datasets for image classification, and compare the results against other various state-of-the-art models to determine the efficacy of our efforts. Our model not only outperforms the existing methods, but it also gives us a narrower range of AUCs for the tested classes, suggesting a stark improvement in both accuracy and precision. Moreover, we discover that introducing this “Novelty Normalization” concept into our model allows us to expand its usage into multiclass scenarios without a steep drop in accuracy.

异常检测是许多实际应用中的一项重要任务，例如在网络安全和监控中。与当今的大多数数据一样，这些领域内数据的大小和维度也在不断增长，这使得开发一种能够准确有效地识别这些数据集中异常的方法变得至关重要。在本文中，我们解决了一类异常检测的问题，在对单个类进行训练后，我们尝试确定输入是否属于该类。大多数当前存在的方法都有局限性，其中新类别的标准仅依赖于重建误差项。我们试图通过建议使用带有归一化项的自动编码器网络来摆脱这种限制。我们在训练过程中将其与附加新颖性评分模块配对，作为确定给定图像与我们确定的正常类别之间差异的一种方式，从而提高我们模型的效率。我们在 MNIST、CIFAR-10 和 Fashion-MNIST（三个流行的图像分类数据集）上评估我们的模型，并将结果与​​其他各种最先进的模型进行比较，以确定我们工作的有效性。我们的模型不仅优于现有方法，而且还为测试类提供了更窄的 AUC 范围，这表明准确性和精确度都有显着提高。此外，我们发现将这种“新颖性归一化”概念引入我们的模型允许我们将其使用扩展到多类场景中，而不会导致准确率急剧下降。