Incorporating attack information into makeup to attack deep learning models
Machine learning has evolved very rapidly, with good results in both computer vision and natural language processing. There are many deep learning techniques that are used in everyday life of humans such as autonomous vehicles and face recognition systems. Nowadays, the gradual dependence of human daily life on deep neural networks can lead to serious consequences, so the security of neural networks becomes very important. Therefore, the deep neural network has obvious weaknesses. We propose a method based on generating a confrontation network to generate a facial makeup picture that can deceive the face recognition system. We hide the perturbation of the attack in the results of the abnormal makeup photos that humans can’t detect. The experimental results show that we can not only generate high-quality facial makeup images, but also our attack results have a high attack success rate in the face recognition system.
