  One way of designing a robust machine learning algorithm is to generate
authentic adversarial images which can trick the algorithms as much as
possible. In this study, we propose a new method to generate adversarial images
which are very similar to true images, yet, these images are discriminated from
the original ones and are assigned into another category by the model. The
proposed method is based on a popular concept of experimental psychology,
called, Just Noticeable Difference. We define Just Noticeable Difference for a
machine learning model and generate a least perceptible difference for
adversarial images which can trick a model. The suggested model iteratively
distorts a true image by gradient descent method until the machine learning
algorithm outputs a false label. Deep Neural Networks are trained for object
detection and classification tasks. The cost function includes regularization
terms to generate just noticeably different adversarial images which can be
detected by the model. The adversarial images generated in this study looks
more natural compared to the output of state of the art adversarial image
generators.
