Detecting Anomalies using Generative Adversarial Networks on Images
- URL: http://arxiv.org/abs/2211.13808v1
- Date: Thu, 24 Nov 2022 21:52:25 GMT
- Title: Detecting Anomalies using Generative Adversarial Networks on Images
- Authors: Rushikesh Zawar, Krupa Bhayani, Neelanjan Bhowmik, Kamlesh Tiwari and
Dhiraj Sangwan
- Abstract summary: This paper proposes a novel Generative Adversarial Network (GAN) based model for anomaly detection.
It uses normal (non-anomalous) images to learn about the normality based on which it detects if an input image contains an anomalous/threat object.
Experiments are performed on three datasets, viz. CIFAR-10, MVTec AD (for industrial applications) and SIXray (for X-ray baggage security)
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Automatic detection of anomalies such as weapons or threat objects in baggage
security, or detecting impaired items in industrial production is an important
computer vision task demanding high efficiency and accuracy. Most of the
available data in the anomaly detection task is imbalanced as the number of
positive/anomalous instances is sparse. Inadequate availability of the data
makes training of a deep neural network architecture for anomaly detection
challenging. This paper proposes a novel Generative Adversarial Network (GAN)
based model for anomaly detection. It uses normal (non-anomalous) images to
learn about the normality based on which it detects if an input image contains
an anomalous/threat object. The proposed model uses a generator with an
encoder-decoder network having dense convolutional skip connections for
enhanced reconstruction and to capture the data distribution. A self-attention
augmented discriminator is used having the ability to check the consistency of
detailed features even in distant portions. We use spectral normalisation to
facilitate stable and improved training of the GAN. Experiments are performed
on three datasets, viz. CIFAR-10, MVTec AD (for industrial applications) and
SIXray (for X-ray baggage security). On the MVTec AD and SIXray datasets, our
model achieves an improvement of upto 21% and 4.6%, respectively
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