Gestalt Aspects of Security Patterns

Abstract

In this contribution, we discuss specific aspects of patterns appearing in a humanized context (as for example in computational security or forensics) and that are not well reflected in a pure feature-classification framework. Gestalt laws are considered as a more appropriate way to approach these aspects, but then, we would merely focus on an empiricial description of matter, where models are needed that might even guide to engineering procedures. For the provision of such procedures, pure image processing based approaches, despite half a century of research, seemingly did not much progress. A recently emerging new family of neural network architectures, based on the model of neural group processing, on the contrary, shows even in its mostly still premature state (from engineering point of view) already a much stronger relevance to the modeling of Gestalt aspects.