## 1 Introduction  
Authenticating identity is a major step in most security systems because access levels and privileges are based on that identity. As authentication measures, including knowledge-based, possession-based, and biometric-based systems, are advancing to offer more robust, accurate, and user-friendly capabilities, attacks on authentication systems have been advancing as well, resulting in the development of the ability to trick an authentication system so that an imposter gains access to resources as a legitimate user. As a result, research has advanced to mitigate such threats and vulnerabilities that an authentication system may experience. Some measures are policy-based, and others are technical. Among all of the authentication systems, biometrics has shown an advantage over other authentication systems, including knowledge- or possession-based systems. For example, biometric-based technologies cannot be forgotten or lost, as opposed to other authentication-based systems. Further, biometric-based systems reveal the identity of a user overtly or covertly, making identity authentication an automated process. Further, biometric systems show a greater robustness against attacks when compared to knowledge- or possession-based systems. Biometric-based technologies include physiological-based measures such as the fingerprint, iris, retina, and face recognition, as well as behavior-based measures such as the signature and gait. Since fingerprint technology is the most used and accepted among users, as opposed to iris- or retina-based technologies, and is more accurate when compared with behavior-based biometric systems [1], it has been adopted by the latest smartphones to authenticate a user and provide them access. This has resulted in attacks focusing on fooling fingerprint-based biometric systems and mainly circumventing the fingerprint sensor. Fingerprint technologies inherit the general known vulnerabilities to attacks on any biometric system, as shown in Figure 1. The first vulnerability is attacking and fooling the sensor, where an attacker provides a fake fingerprint. Numerous approaches tackle fake fingerprint attacks using liveness detection, where a system determines if a provided fingerprint is alive or fake. Some examples of fingerprint liveness detection include pulse rate, pore changes over time, and oxygen-level detection. Further, the sensor level attacks include forcing and coercing a user to place their fingerprint to authenticate and gain access. A few solutions were proposed, such as using a specific finger to authenticate, which provides limited access or access to a fake interface while reporting the incident. The second inherited vulnerability in biometric systems is attacking the channel between the sensor and the feature extractor, where an attacker records the signals that are sent from the sensor and then replays the signals to fool the biometric system. A few solutions were provided such as implementing a challenge-response metric where a sensor sends a unique seed number each time it is used, and a feature extractor validates the seed number before accepting and analyzing the signal that is sent to the feature extractor. The third vulnerability is attacking the feature extractor component with an override attack, where a feature extractor does not extract any features but limited ones from the fingerprint where any fingerprint may match a stored template. The fourth vulnerability is attacking the channel between the feature extractor and the matcher, where a synthesized feature vector is embedded into the channel, and the matcher will always report a high matching score for the attacker to gain access. The fifth vulnerability is in attacking the channel between the feature extractor and the template database with a channel interception, where a template is modified or altered prior to storage. The sixth vulnerability is in attacking the template database itself by replacing templates with the attacker's template.