Disclosed herein is attack-less adversarial training for robust adversarial defense. The attack-less adversarial training for robust adversarial defense includes the steps of: (a) generating individual intervals (ci) by setting the range of color (C) and then discretizing the range of color (C) by a predetermined number (k); (b) generating one batch from an original image (X) and training a learning model with the batch; (c) predicting individual interval indices (ŷialat) from respective pixels (xi) of the original image (X) by using an activation function; (d) generating a new image (Xalat) through mapping and randomization; and (e) training a convolutional neural network with the image (Xalat) generated in step (d) and outputting a predicted label (Ŷ).
ATTACK-LESS ADVERSARIAL TRAINING FOR ROBUST ADVERSARIAL DEFENSE