# Palmprint False Acceptance Attack with a Generative Adversarial Network (GAN)
## 1 Introduction  
Today, people are enjoying variety of internet services that are associated to the advancement of telecommunication, smart devices, small IoT devices, and social media. For the growth of multimedia in future, multimedia security should be braced with the supporting technologies, such as identity management. Unlike traditional identity management approaches that require users to supply their own credentials and/or known-knowledge for authentication, biometric systems offer better usability [1, 2] and have become versatile [3]. However, biometric systems are vulnerable to several attacks, such as image-level attack. In image-level attacks, the attackers try to find or artificially counterfeit the fake biometric images that can successfully cheat the systems, which can be used to impersonate genuine users [4]. Two typical instances are reconstruction attack (RA) [5] and presentation attack (PA) [6]. In RA and PA, only \"similarity\" is considered while additional significant evaluation of \"naturalness\" is neglected. \"Similarity\" refers to the closeness of two biometric templates in terms of a distance metric. In image-level attacks, the two biometric templates are generated from a genuine users' original biometric image and a found/counterfeited fake biometric image. If \"similarity\" is satisfied, the image-level attack is successful; i.e., the genuine user is impersonated successfully. On the other hand, \"naturalness\" refers to a requirement where a found/counterfeited fake image must look natural, which implies the image should not have strong noise or look artificial. The counterfeited image without sufficient naturalness can be easily detected by a human operator or artificial intelligence machine. \"Naturalness\" means an image seems natural rather than counterfeited. If an image has strong noise or noiselike appearance, it seems remarkably counterfeited. An image without sufficient naturalness can be easily detected and resisted against. The three biometric image-level attacks, namely RA, PA, and FAA, are discussed in terms of the two aforementioned evaluations and other performance, as follows. RA is shown in Figure 1. A biometric image is reconstructed from a genuine user's target feature template in feature domain. Some evolution algorithms, such as the genetic algorithm (GA) or hill-climbing (HC) algorithm, are used to iteratively modify the fake image in image domain and continuously enhance the similarity in feature domain until RA is successful. Typically, if RA is successful, the target feature template and fake feature template are similar, while the target image and the fake image are dissimilar, because the similarity in feature domain does not ensure the similarity in image domain. In addition, since the naturalness is not considered in RA, the reconstructed fake images typically have insufficient naturalness; i.e., they have a counterfeit appearance that reveals they are not captured in natural environments. Furthermore, the evolution algorithms in RA become invalid in biocryptosystems if the extracted/recovered bio-key is strictly protected with one-way function. The correlation between the input and output is completely damaged by one-way function, so the evolution algorithms in RA cannot enhance the similarity in feature domain. PA is shown in Figure 2. In PA, the attackers must have a genuine user' original target biometric image, and counterfeit a fake biometric image on some spoofing carriers, including print the image on a piece of paper, display the biometric image on a monitor, counterfeit a mask for a face or a glove for palmprint. They use the spoofing carriers containing the genuine users' original target biometric images, such as the paper, monitor, mask, and glove, to cheat the biometric system. It is easy to capture genuine users' original target face images or obtain them on internet social networks, but sometimes it is difficult to gain genuine users' original target images of other biometric modalities, such as palmprint.