When deep learning mistakes a coffee-maker for a cobra

'Is this your sister'' That's the kind of question asked by image-recognition systems, which are becoming increasingly prevalent in our everyday devices. They may soon be used for tumor detection and genomics, too. These systems rely on what is known as 'deep-learning' architectures - an exciting new development in artificial learning. But EPFL researchers have revealed just how sensitive these systems actually are: a tiny universal perturbation applied across an image can throw off even the most sophisticated algorithms. Deep-learning systems, a major breakthrough in computer-based image recognition, are however surprisingly sensitive to minor changes in the data they analyze. Researchers at EPFL's Signal Processing Laboratory (LTS4), headed by Pascal Frossard, have shown that even the best deep-learning architectures can be fooled by introducing an almost invisible perturbation into digital images. Such a perturbation can cause a system to mistake a joystick for a Chihuahua, for example, or a coffee-maker for a cobra.