Camera Tampering Detection using Generative Reference Model and Deep Learned Features

Pranav Mantini, Shishir K. Shah

2019

Abstract

An unauthorized alteration in the viewpoint of a surveillance cameras is called tampering. This involves comparing images from the surveillance camera against a reference model. The reference model represents the features (e.g. background, edges, and interest points) of the image under normal operating conditions. The approach is to identify a tamper by analysing the distance between the features of the image from surveillance camera and from the reference model. If the distance is not within a certain threshold, the image is labeled as a tamper. Most methods have used images from the immediate past of the surveillance camera to construct the reference model. We propose to employ a generative model that learns the distribution of images from the surveillance camera under normal operating conditions, by training a generative adversarial network (GAN). The GAN is capable of sampling images from the probability density function, which are used as reference. We train a Siamese network that transforms the images into a feature space, so as to maximize the distance between the generated images and tampered images (while minimizing the distance between generated and normal images). The distance between the generated and the surveillance camera image is classified as either normal or tampered. The model is trained and tested over a synthetic dataset that is created by inducing artificial tampering (using image processing techniques). We compare the performance of the proposed model against two existing methods. Results show that the proposed model is highly capable of detecting and classifying tampering, and outperforms the existing methods with respect to accuracy and false positive rate.

Paper Citation

in Harvard Style

Mantini P. and Shah S. (2019). Camera Tampering Detection using Generative Reference Model and Deep Learned Features. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 85-95. DOI: 10.5220/0007392100850095

in Bibtex Style

@conference{visapp19,
author={Pranav Mantini and Shishir K. Shah},
title={Camera Tampering Detection using Generative Reference Model and Deep Learned Features},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP},
year={2019},
pages={85-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007392100850095},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP
TI - Camera Tampering Detection using Generative Reference Model and Deep Learned Features
SN - 978-989-758-354-4
AU - Mantini P.
AU - Shah S.
PY - 2019
SP - 85
EP - 95
DO - 10.5220/0007392100850095
PB - SciTePress