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Automatic detection of anomalies such as weapons or threat objects in baggagesecurity, or detecting impaired items in industrial production is an importantcomputer vision task demanding high efficiency and accuracy. Most of theavailable data in the anomaly detection task is imbalanced as the number ofpositive/anomalous instances is sparse. Inadequate availability of the datamakes training of a deep neural network architecture for anomaly detectionchallenging. This paper proposes a novel Generative Adversarial Network (GAN)based model for anomaly detection. It uses normal (non-anomalous) images tolearn about the normality based on which it detects if an input image containsan anomalous/threat object. The proposed model uses a generator with anencoder-decoder network having dense convolutional skip connections forenhanced reconstruction and to capture the data distribution. A self-attentionaugmented discriminator is used having the ability to check the consistency ofdetailed features even in distant portions. We use spectral normalisation tofacilitate stable and improved training of the GAN. Experiments are performedon three datasets, viz. CIFAR-10, MVTec AD (for industrial applications) andSIXray (for X-ray baggage security). On the MVTec AD and SIXray datasets, ourmodel achieves an improvement of upto 21% and 4.6%, respectively

Detecting Anomalies using Generative Adversarial Networks on Images