  As an important perceptual characteristic of the Human Visual System (HVS),
the Just Noticeable Difference (JND) has been studied for decades with image
and video processing (e.g., perceptual visual signal compression). However,
there is little exploration on the existence of JND for the Deep Machine Vision
(DMV), although the DMV has made great strides in many machine vision tasks. In
this paper, we take an initial attempt, and demonstrate that the DMV has the
JND, termed as the DMV-JND. We then propose a JND model for the image
classification task in the DMV. It has been discovered that the DMV can
tolerate distorted images with average PSNR of only 9.56dB (the lower the
better), by generating JND via unsupervised learning with the proposed
DMV-JND-NET. In particular, a semantic-guided redundancy assessment strategy is
designed to restrain the magnitude and spatial distribution of the DMV-JND.
Experimental results on image classification demonstrate that we successfully
find the JND for deep machine vision. Our DMV-JND facilitates a possible
direction for DMV-oriented image and video compression, watermarking, quality
assessment, deep neural network security, and so on.
