Biblio

This paper proposes a method for detecting anomalies in video data. A Variational Autoencoder (VAE) is used for reducing the dimensionality of video frames, generating latent space information that is comparable to low-dimensional sensory data (e.g., positioning, steering angle), making feasible the development of a consistent multi-modal architecture for autonomous vehicles. An Adapted Markov Jump Particle Filter defined by discrete and continuous inference levels is employed to predict the following frames and detecting anomalies in new video sequences. Our method is evaluated on different video scenarios where a semi-autonomous vehicle performs a set of tasks in a closed environment.

The natural redundancy in video data due to its spatio-temporal correlation of neighbouring pixels require highly complex encryption process to successfully cipher the data. Conventional encryption methods are based on lengthy keys and higher number of rounds which are inefficient for low powered, small battery operated devices. Motivated by the success of lightweight encryption methods specially designed for IoT environment, herein an efficient method for video encryption is proposed. The proposed technique is based on a recently proposed encryption algorithm named Secure IoT (SIT), which utilizes P and Q functions of the KHAZAD cipher to achieve high encryption at low computation cost. Extensive simulations are performed to evaluate the efficacy of the proposed method and results are compared with Secure Force (SF-64) cipher. Under all conditions the proposed method achieved significantly improved results.

Identity masking methods have been developed in recent years for use in multiple applications aimed at protecting privacy. There is only limited work, however, targeted at evaluating effectiveness of methods-with only a handful of studies testing identity masking effectiveness for human perceivers. Here, we employed human participants to evaluate identity masking algorithms on video data of drivers, which contains subtle movements of the face and head. We evaluated the effectiveness of the “personalized supervised bilinear regression method for Facial Action Transfer (FAT)” de-identification algorithm. We also evaluated an edge-detection filter, as an alternate “fill-in” method when face tracking failed due to abrupt or fast head motions. Our primary goal was to develop methods for humanbased evaluation of the effectiveness of identity masking. To this end, we designed and conducted two experiments to address the effectiveness of masking in preventing recognition and in preserving action perception. 1- How effective is an identity masking algorithm?We conducted a face recognition experiment and employed Signal Detection Theory (SDT) to measure human accuracy and decision bias. The accuracy results show that both masks (FAT mask and edgedetection) are effective, but that neither completely eliminated recognition. However, the decision bias data suggest that both masks altered the participants' response strategy and made them less likely to affirm identity. 2- How effectively does the algorithm preserve actions? We conducted two experiments on facial behavior annotation. Results showed that masking had a negative effect on annotation accuracy for the majority of actions, with differences across action types. Notably, the FAT mask preserved actions better than the edge-detection mask. To our knowledge, this is the first study to evaluate a deidentification method aimed at preserving facial ac- ions employing human evaluators in a laboratory setting.