Style Your Face Morph and Improve Your Face Morphing Attack Detector
This work addresses security vulnerabilities in face recognition systems by enhancing morphing attacks, which is an incremental improvement in biometric spoofing techniques.
The paper tackles the problem of improving the quality of morphed face images to enhance their effectiveness in bypassing biometric verification systems, and finds that detection systems perform significantly worse when confronted with these improved images, with most systems showing enhanced robustness after training on them.
A morphed face image is a synthetically created image that looks so similar to the faces of two subjects that both can use it for verification against a biometric verification system. It can be easily created by aligning and blending face images of the two subjects. In this paper, we propose a style transfer based method that improves the quality of morphed face images. It counters the image degeneration during the creation of morphed face images caused by blending. We analyze different state of the art face morphing attack detection systems regarding their performance against our improved morphed face images and other methods that improve the image quality. All detection systems perform significantly worse, when first confronted with our improved morphed face images. Most of them can be enhanced by adding our quality improved morphs to the training data, which further improves the robustness against other means of quality improvement.