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   Authentication systems are vulnerable to model inversion attacks where an adversary is able to approximate the inverse of a target machine learning model. Biometric models are a prime candidate for this type of attack. This is because inverting a biometric model allows the attacker to produce a realistic biometric input to spoof biometric authentication systems. One of the main constraints in conducting a successful model inversion attack is the amount of training data required. In this work, we focus on iris and facial biometric systems and propose a new technique that drastically reduces the amount of training data necessary. By leveraging the output of multiple models, we are able to conduct model inversion attacks with 1/10th the training set size of Ahmad and Fuller (IJCB 2020) for iris data and 1/1000th the training set size of Mai et al. (Pattern Analysis and Machine Intelligence 2019) for facial data. We denote our new attack technique as structured random with alignment loss. 

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   https://doi.org/10.1093/gji/ggac285  

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   SUMMARY Seamounts are volcanic constructs that litter the seafloor. They are important for understanding numerous aspects of marine science, such as plate tectonics, the volcanic melt budget, oceanic circulation, tsunami wave diffraction, tidal energy dissipation and mass wasting. Geometrically, seamounts come in many sizes and shapes, and for the purpose of modelling them for morphological, gravimetric or isostatic studies it is convenient to have simple analytical models whose properties are well known. Here, we present a family of seamount models that may be used in such studies, covering both the initial construction phase and later mass-wasting by sectoral collapses. We also derive realistic axisymmetric density variations that are compatible with observed first-order structure from seismic tomography studies. 

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