Semantic Face Transformations for Attacking Deep Neural Networks and Improving Robustness

Qilin Zhang, Younghoon Kim

http://doi.org/10.5626/JOK.2021.48.7.809

Deep neural networks(DNNs) have achieved great successes in various vision fields such as autonomous driving, face recognition, and object detection. However, a well-trained network can be manipulated if the input of the deep neural networks is disturbed by perturbations. Currently a common attack method is by adding perturbations to the pixel space of images by limiting the Lp-norm of the perturbations. Pixel-based transformations are easily detected by the naked eye so a realistic effective attack can be a method of disturbing the network by unnaturally transforming the image. In this paper, we proposed a new attack method to use natural color transformation through the segmentation of face images. We generated face transformation images based on semantic face transformation and conducted comprehensive experiments to show that using our face transformation reduced the accuracy rate of the classification network. Our face transformation images were also used for robustness training of the neural network. The robustness of the deep neural network was improved.