Data Driven Computing by the Morphing Fast Fourier Transform Ensemble Kalman Filter in Epidemic Spread Simulations

The FFT EnKF data assimilation method is proposed and applied to a stochastic cell simulation of an epidemic, based on the S-I-R spread model. The FFT EnKF combines spatial statistics and ensemble filtering methodologies into a localized and computationally inexpensive version of EnKF with a very small ensemble, and it is further combined with the morphing EnKF to assimilate changes in the position of the epidemic.Comment: 11 pages, 3 figures. Submitted to ICCS 201

Single and Differential Morph Attack Detection

Face recognition systems operate on the assumption that a person\u27s face serves as the unique link to their identity. In this thesis, we explore the problem of morph attacks, which have become a viable threat to face verification scenarios precisely because of their inherent ability to break this unique link. A morph attack occurs when two people who share similar facial features morph their faces together such that the resulting face image is recognized as either of two contributing individuals. Morphs inherit enough visual features from both individuals that both humans and automatic algorithms confuse them. The contributions of this thesis are two-fold: first, we investigate a morph detection methodology that utilizes wavelet sub-bands to differentiate bona fide and morph images. Second, we investigate the usefulness of morphing identical twins to train a network robustly. Although not always discernible in the image domain, many morphing algorithms introduce artifacts in the final image that can be leveraged for morph attack detection. Because wavelet decomposition allows us to separately examine low and high frequency data, we can identify and isolate these morphing artifacts in the spatial frequency domain. To this end, a wavelet-based deep learning approach to detect morph imagery is proposed and evaluated. We examine the efficacy of wavelet sub-bands for both single and differential morph attack detection and compare performance to other methods in the literature. Finally, experiments are done on a large scale morph dataset created using twins. This high quality morph twins dataset is used to train a single morph detector. The details of this detector are explained and the resulting morph detector is submitted to the NIST FRVT test for objective evaluation, where our detector exhibited promising results

Audio style transfer

'Style transfer' among images has recently emerged as a very active research topic, fuelled by the power of convolution neural networks (CNNs), and has become fast a very popular technology in social media. This paper investigates the analogous problem in the audio domain: How to transfer the style of a reference audio signal to a target audio content? We propose a flexible framework for the task, which uses a sound texture model to extract statistics characterizing the reference audio style, followed by an optimization-based audio texture synthesis to modify the target content. In contrast to mainstream optimization-based visual transfer method, the proposed process is initialized by the target content instead of random noise and the optimized loss is only about texture, not structure. These differences proved key for audio style transfer in our experiments. In order to extract features of interest, we investigate different architectures, whether pre-trained on other tasks, as done in image style transfer, or engineered based on the human auditory system. Experimental results on different types of audio signal confirm the potential of the proposed approach.Comment: ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2018, Calgary, France. IEE