# Palmprint False Acceptance Attack with a Generative Adversarial Network (GAN)
## 1 Introduction
Some biometrics, such as veins or electrical biosignals, can resist PA and be used for liveness detection due to their concealed nature [7]. Figure 1: Reconstruction attack. FAA is shown in Figure 3, which does not have the aforementioned issues that were presented in RA and PA. FAA does not require the genuine users' images, and it generates a large number of fake palmprint images with image generation technologies, such as generative adversarial network (GAN) and variational autoencoder (VAE), to impersonate genuine users. The fake images used to impersonate genuine users consist of the fake image set. The attackers try to find the fake image in fake image set to satisfy the similarity in feature domain; i.e., the fake image found can generate the fake feature template that is similar to the target feature template. Although biometrics is a powerful tool widely deployed in various security systems, biometric characteristics are largely immutable, resulting in permanent biometric compromise when a feature template is stolen. Thus, the usage of feature templates without protection should be forbidden. Cancellable biometric and biocytosystem are two main pipelines for biometric template protection, whose frameworks are shown in Figures 4 and 5, respectively. The similarity between the feature templates in feature domain is approximatively preserved between the cancellable templates in cancellable domain, so the evolution algorithms in RA are valid for a cancellable biometric [8]. In biocytosystems, a bio-key can be extracted/recovered and protected with a one-way function, such as hash function. RA becomes invalid because one-way functions completely damage the correlation/linkage between the input and output. However, PA uses the spoofing carriers containing the genuine users' original target biometric images to cheat biometric systems; while FAA uses the fake image set to cheat biometric systems. Neither PA nor FAA requires the data in feature domain, cancellable domain, and hash domain. Thus, PA and FAA are still valid in biocytosystems. If a fake feature template is similar enough to a target feature template, i.e., the similarity in feature domain is satisfied, the bio-key in bio-key domain can be extracted/recovered from the fake feature template. Figure 3: False acceptance attack. Figure 2: Presentation attack. Palmprint refers to the inner surface from the fingers to the wrist, which contains rich unique personal features, such as ridges, minutiae, and textures, so it is discriminative, noninvasive, stable, acceptable, and has good privacy. In addition, many feature extractors of other biometric modalities are suitable for palmprint, so palmprint is an important and representative biometric modality. In this paper, we develop false acceptance attack (FAA) for a palmprint biometric. The contributions of this paper include:  
* We develop FAA for palmprint biometrics and demonstrate its feasibility against coding-based palmprint biometric systems. The FAA is free from the issues found in RA and PA.
* FAA does not require genuine users' images, and it can be launched simply with the synthetic images with high naturalness, which are generated by the generative adversarial networks. The naturalness of the reconstructed images is neglected in PA, so the FAA is a more fraudulent attack than RA. * To further improve the efficiency of FAA, we employ a clustering method to select diverse fake images in order to enhance the diversity of the fake images used, so the number of attack attack times is reduced; i.e., the attackers can quickly find the fake image in a fake image set, which can cheat the system successfully.