The images that you provided may be unrecognizable for us. They are actually the images that we recognize but evolved using the Sferes evolutionary framework.
While these images are almost impossible for humans to label with anything but abstract arts, the Deep Neural Network will label them to be familiar objects with 99.99% confidence.
This result highlights differences between how DNNs and humans recognize objects. Images are either directly (or indirectly) encoded
According to this video
Changing an image originally correctly classified in a way imperceptible to humans can cause the cause DNN to classify it as something else.
In the image below the number at the bottom are the images are supposed to look like the digits But the network believes the images at the top (the one like white noise) are real digits with 99.99% certainty.
The main reason why these are easily fooled is that Deep Neural Network does not see the world in the same way as human vision. We use the whole image to identify things while DNN depends on the features. As long as DNN detects certain features, it will classify the image as a familiar object it has been trained on. The researchers proposed one way to prevent such fooling by adding the fooling images to the dataset in a new class and training DNN on the enlarged dataset. In the experiment, the confidence score decreases significantly for ImageNet AlexNet. It is not easy to fool the retrained DNN this time. But when the researchers applied such method to MNIST LeNet, evolution still produces many unrecognizable images with confidence scores of 99.99%.
