Deep neural networks are powerful machine learning approaches that have exhibited excellent results on many classification tasks. However, they are considered 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 understand why an image is recognized or not. In this study, we formalize some properties shared by eight state-of-the-art deep neural networks in order to grasp the principles allowing a given deep neural network to classify an image. Our results, tested on these eight networks, show that an image can be sub-divided into several regions (patches) responding at different degrees of probability (local property). With the same patch, some locations in the image can answer two (or three) orders of magnitude higher than other locations (spatial property). 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 (cumulative property). Furthermore, we propose a new approach called Deepception that exploits these properties to deceive a deep neural network. We obtain for the VGG-VDD-19 neural network a fooling ratio of 88 . Thanks to our "Psychophysics" approach, no prior knowledge on the networks architectures is required.
Ernst Mach (1838–1916) performed pioneering research on vestibular function 100 years ago. His experiments were mainly psychophysical and included measurements of threshold and study of the vestibular We can better understand deep neural networks by identifying which features each of their neurons have learned to detect. To do so, researchers have created Deep Visualization techniques including activation maximization, which synthetically generates inputs (e.g. images) that maximally activate each neuron. A limitation of current techniques is that they assume each neuron detects only one type of feature, but we know that neurons can be multifaceted, in that they fire in response to many different types of features: for example, a grocery store class neuron must activate either for rows of produce or for a storefront. Previous activation maximization techniques constructed images without regard for the multiple different facets of a neuron, creating inappropriate mixes of colors, parts of objects, scales, orientations, etc. Here, we introduce an algorithm that explicitly uncovers the multiple facets of each neuron by producing a synthetic visualization of each of the types of images that activate a neuron. We also introduce regularization methods that produce state-of-the-art results in terms of the interpretability of images obtained by activation maximization. By separately synthesizing each type of image a neuron fires in response to, the visualizations have more appropriate colors and coherent global structure. Multifaceted feature visualization thus provides a clearer and more comprehensive description of the role of each neuron.  Recent years have produced great advances in training large, deep neural networks (DNNs), including notable successes in training convolutional neural networks (convnets) to recognize natural images. However, our understanding of how these models work, especially what computations they perform at intermediate layers, has lagged behind. Progress in the field will be further accelerated by the development of better tools for visualizing and interpreting neural nets. We introduce two such tools here. The first is a tool that visualizes the activations produced on each layer of a trained convnet as it processes an image or video (e.g. a live webcam stream). We have found that looking at live activations that change in response to user input helps build valuable intuitions about how convnets work. The second tool enables visualizing features at each layer of a DNN via regularized optimization in image space. Because previous versions of this idea produced less recognizable images, here we introduce several new regularization methods that combine to produce qualitatively clearer, more interpretable visualizations. Both tools are open source and work on a pre-trained convnet with minimal setup.  Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark. However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we address both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. We also perform an ablation study to discover the performance contribution from different model layers. This enables us to find model architectures that outperform Krizhevsky al on the ImageNet classification benchmark. We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets. properly with psychophysics, the hundred-year-old discipline con cerned with the responses that organisms make to the energies of the environment. We live in a restless world of energetic forces, some of which affect us and some of which, like radio waves, impinge upon us and pass unnoticed because we have no sense organs able to transduce them. But we see lights, hear sounds, taste substances, and smell vapors; and it is these elementary facts of psychophysics that stir our interest in the anatomy and physiology of the mechanisms that make sensation possible. An orderly and systematic account of sensory communication must include a delineation of what is perceived as well as an explanation of how perception is accomplished. In this sense, psychophysics defines the challenge : it tells what the organism can do and it asks those who are inspired by such mysteries to try, with scalpel, electrode, and test tube, to advance our understanding of how such wonders are performed. It must be confessed at the outset that psychophysics has often failed to do its part of the job with distinction. Its task is not easy. For one thing, long-standing prejudices, derived in great measure from a chronic dualistic metaphysics, have triggered a variety of stubborn objections whenever it has been proposed that sensation may be amenable to orderly and quantitative investigation. You cannot, the objectors complain, measure the inner, private, subjective strength of a sensation. Perhaps not, in the sense the objectors have in mind, but in a different and very useful sense the strength of a sensation can, as we shall see, be fruitfully quantified. We must forego arguments about the private life of the mind and ask sensible objective questions about the input-output relations of sensory transducers as these relations are disclosed in the behavior of experimental organisms, whether men or animals. Another difficulty is that psychophysics had an unfortunate child hood. Although Plateau, in the 1850's, made a half-hearted attempt to suggest the proper form of the function relating apparent sensory intensity to stimulus intensity, he was shouted down by Fechner, who saddled the infant discipline with the erroneous "law" that bears his 