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Deep neural networks are powerful machine learning approaches that haveexhibited excellent results on many classification tasks. However, they areconsidered as black boxes and some of their properties remain to be formalized.In the context of image recognition, it is still an arduous task to understandwhy an image is recognized or not. In this study, we formalize some propertiesshared by eight state-of-the-art deep neural networks in order to grasp theprinciples allowing a given deep neural network to classify an image. Ourresults, tested on these eight networks, show that an image can be sub-dividedinto several regions (patches) responding at different degrees of probability(local property). With the same patch, some locations in the image can answertwo (or three) orders of magnitude higher than other locations (spatialproperty). Some locations are activators and others inhibitors(activation-inhibition property). The repetition of the same patch can increase(or decrease) the probability of recognition of an object (cumulativeproperty). Furthermore, we propose a new approach called Deepception thatexploits these properties to deceive a deep neural network. We obtain for theVGG-VDD-19 neural network a fooling ratio of 88\%. Thanks to our"Psychophysics" approach, no prior knowledge on the networks architectures isrequired.

Out of the Black Box: Properties of deep neural networks and their applications