The Network Security Lab team at the University of Washington has discovered a reputed weakness of deep neural networks (https://arxiv.org/pdf/1703.06857v1.pdf).  DNN seem to have problems with identifying negative images. 

If the network was trained to recognize three images in white, it was not trained to recognize them in black. The researchers perceive here a weakness in the ability of the DNN to generalize. A simple solution to this problem would be to create the negative examples for the automated training.  

However, the question that arises is how far a generalization of the network is desired at all. For color images, it is important that the network does not ignore the color information. The network should have a general view i.e. the red color, but still be able to distinguish red and green. This shows the major role that the careful selection of the training set and the learning function play in order to train the net correctly to your own needs.