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Privacy-Preserving Analysis for Remote
Video Anomaly Detection in Real Life Environments
Institute for Informatics and Telematics, National Research Council of Italy, Pisa,
Italy Abstract This paper proposes a novel approach for
privacy-preserving surveillance video streams anomaly detection, i.e.,
situations implying violence, illegal actions, or situations involving
hazards. In particular, this approach adopts a privacy-preserving mechanism
based on autoencoder neural networks applied in a
differential private manner, exploiting three different types of differential
private optimizers. Recorded real-world video streams are segmented into data
frames, which are compressed into special codes with autoencoders
and differential privacy and transmitted to a central server where they get
decoded into an anonymized version of the original data frame that can be
analyzed to detect anomalies. The anomaly detection algorithm exploits a
supervised learning binary classification methodology of extracted
contextual, spatial, and motion data on imbalanced datasets. Anomalies are
differentiated into ”soft” and ”hard”, and the anomaly detection score is
computed based on a sigmoidal function. The proposed methodology has been validated with a set of experiments on a
well-known video anomaly dataset: UCF-CRIME. The experiments we conducted on
the testbed demonstrate the capability of the system to
correctly identify video anomalies, with a consistent privacy gain
demonstrated by the strongly reduced ability to identify people from faces in
the reconstructed frames. Keywords: Anomaly
detection, Autoencoders, Behavioral analysis, Deep
Learning, Computer Vision, Differential Privacy, Trustworthy Artificial
Intelligence +: Corresponding author: Giacomo Giorgi Journal
of Wireless Mobile Networks, Ubiquitous Computing, and Dependable
Applications (JoWUA) Received: December 19, 2021; Accepted: February 10,
2022; Published: March 31, 2022 DOI: 10.22667/JOWUA.2022.03.31.112 |