  As an important perceptual characteristic of the Human Visual System (HVS),
the Just Noticeable Difference (JND) has been studied for decades with
image/video processing (e.g., perceptual image/video coding). However, there is
little exploration on the existence of JND for AI, like 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 DMV does have the
JND, termed as DMVJND. Besides, we propose a JND model for the classification
task in DMV. It has been discovered that DMV can tolerate distorted images with
average PSNR of only 9.56dB (the lower the better), by generating JND via
unsupervised learning with our DMVJND-NET. In particular, a semantic-guided
redundancy assessment strategy is designed to constrain the magnitude and
spatial distribution of the JND. Experimental results on classification tasks
demonstrate that we successfully find and model the JND for deep machine
vision. Meanwhile, our DMV-JND paves a possible direction for DMV oriented
image/video compression, watermarking, quality assessment, deep neural network
security, and so on.
