Adversarial attacks by attaching noise markers on the face against deep face recognition

초록

Deep neural networks (DNNs) have become increasingly effective in difficult machine learning tasks, such as image classification, speech recognition, and natural language processing. Face recognition (FR) using DNNs shows high performance and is widely used in various domains such as payment systems and immigration inspection. However, DNNs are vulnerable to adversarial examples generated by adding a small amount of noise to an original sample, resulting in misclassification by the DNNs. In this study, we attempt to deceive state-of-the-art FR by attaching noise markers on a face in the real world. To deceive an FR model in the real world, we address challenges in the attack process, including selection of locations of noise markers, the differences between colors of digital noise markers and those of noise markers after printing, the differences between the colors of noise markers that are attached to the face and those of noise markers after a picture is taken, and the differences between the locations of digital noise markers and those of noise markers that are attached to the face. In experiments, we generate noise markers considering these challenges and show that state-of-the-art FR can be deceived by attaching a maximum of 10 noise markers to a face. This can cause a security risk for FR models using DNNs.

키워드