  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 model detects the
change in the image by outputting a false label. The noise added to the
original image is defined as the gradient of the cost function of the model. A
novel cost function is defined to explicitly minimize the amount of
perturbation applied to the input image while enforcing the perceptual
similarity between the adversarial and input images. For this purpose, the cost
function is regularized by the well-known total variation and bounded range
terms to meet the natural appearance of the adversarial image. 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 JND method are both more successful in deceiving the
recognition/detection models and less perturbed compared to the images
generated by the state-of-the-art methods, namely, FGV, FSGM, and DeepFool
methods.
