## 2 Literature Review
### Approaches for Fingerprint-Based Coerced User Attacks Detection on the Sensor Level  
Although biometric systems provide convenience and a high level of security, and because the presentation of the biometric features is not always subject to a user's acceptance or denial when compared to knowledge- or possession-based authentication mechanisms, users are subject to coercion, wherein a user, at gunpoint, is forced to present their fingerprint to provide an attacker access. The research community provided various solutions to address this specific sensor-level attack. The authors in [10] provide a general overview of coercion detection in biometric systems and indicate that there are three techniques that can be used to detect coercion: involuntary, where a system automatically detects coercion based on analyzing the user's behavior, a voluntary technique where a user provides indications of coercion, and environmental techniques, where cameras or proximity sensors detect multiple individuals close to an authentication system. The authors further detail several considerations when designing a coercion detection system, such as performance implications, where systems require more than 15 min of gathered data to determine coercion. The authors state that lying, stress, and fear are the primary emotions that accompany coercion. However, the accuracy of detecting a lie, or stress and fear, has a low accuracy of 71% and 82%, respectively. Other research has reached 90% accuracy in detecting these emotions [24]. The other consideration is the diversity of operating devices and their configurations that impact the accuracy of a coercion detection system, as well as cultural, medical, and user acceptance for gathering data to detect coercion. One of the novel techniques for detecting coercion is the tangible key technique (TKT), where a user can trigger a coercion detection by pressing a button. The other techniques involve skin conductivity responses to detect a user's emotions, such as stress and fear. The third technique is the intentional false authentication (IFA), which is widely adopted by banks, where a user clicks or writes their password reversed or authenticates using another fingerprint to trigger the coercion detection system. Finally, there is the facial micro-movement (FMM) method, where micro-movements caused by facial micro-expressions are observed to detect fear. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Accuracy** & **Ref.** \\ \hline Binarized statistical image feature with sampling from Gaussian distribution & 91\% & [11] \\ Joint time frequency analysis & 100\% & [12] \\ CNN & 95\% & [13] \\ CNN-Enhanced & 98\% & [14] \\ Slim-ResCNN & 95.25\% & [15] \\ CNN & 95.2\% & [16] \\ Density-connected CNN & 98.22\% & [17] \\ CNN & 95.5\% & [18] \\ Template-probe CNN & 97.24\% & [19] \\ Score-level fusion & 96.88\% & [20] \\ ECG Fusion & 97.4\% & [21] \\
2-Layer Parallel SVM & 95.32\% & [22] \\ SVM & 100\% & [23] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of methods described in the literature and the accuracy achieved for Liveness Detection. Further, the authors in [25] provided an analysis of coercion resistance in a wiretapping coercer by analyzing changes in skin conductance while authenticating to detect the change in the user's emotion as a sign of coercion. Although numerous valid countermeasures were proposed to safeguard a system from sensor-level attacks, such as developing liveness detection, coercion, where a legitimate user is forced to place their fingerprint on the sensor, has not been widely covered. The convenience of current fingerprint technology, by authenticating a legitimate user in less than a second, has led to the possibility of placing the sensor on the legitimate user's finger to gain illegitimate access.