conclusions figure failure of an image-capt ioning syst em based on deep learning the boy is holding baseball bat in particularthis is highlighted by adversarial exampleswhich are samples fed to deep-learning network that are designed to trick the model into misclassifying them you're already aware thatfor instanceit' possible to do gradient ascent in input space to generate inputs that maximize the activation of some convnet filter--this is the basis of the filter-visualization technique introduced in as well as the deepdream algorithm in similarlythrough gradient ascentyou can slightly modify an image in order to maximize the class prediction for given class by taking picture of panda and adding to it gibbon gradientwe can get neural network to classify the panda as gibbon (see figure this evidences both the brittleness of these models and the deep difference between their input-to-output mapping and our human perception (xpanda panda (xgibbon class gradient gibbonadversarial example figure an adversarial exampleimperceptible changes in an image can upend model' classificat ion of he image licensed to