  In this study, we introduce a measure for machine perception, inspired by the
concept of Just Noticeable Difference (JND) of human perception. Based on this
measure, we suggest an adversarial image generation algorithm, which
iteratively distorts an image by an additive noise until the machine learning
model detects the change in the image by outputting a false label. The amount
of noise added to the original image is defined as the gradient of the cost
function of the machine learning model. This cost function explicitly minimizes
the amount of perturbation applied on the input image and it is regularized by
bounded range and total variation functions to assure perceptual similarity of
the adversarial image to the input. We evaluate the adversarial images
generated by our algorithm both qualitatively and quantitatively on CIFAR10,
ImageNet, and MS COCO datasets. Our experiments on image classification and
object detection tasks show that adversarial images generated by our method are
both more successful in deceiving the recognition/detection model and less
perturbed compared to the images generated by the state-of-the-art methods.
