## 4 Results and Discussion
### Security Vulnerability Assessment  
The vulnerable points of the facial mechanism, used in the present research, can be found both at the level of the structural elements and at the level of the authentication process stages, starting from the process of enrollment, verification, and authorization, and including the database information storage and the data transmission channel, as can be seen in Figure 6.  
Even though biometric authentication systems represent one of the most secure and robust ways of authentication, their accuracy can be negatively influenced by several intrinsic failures such as position, make-up, illness, lighting conditions, facial expression, aging process, the heterogeneity of which can affect the sensor's ability to capture the data successfully, which implies the risk of rejection of a user enrolled in the system.  
The first vulnerable point of the mechanism, exploited by attackers who want to penetrate the system through various techniques and attack tools to manipulate digital facial features, present from the early phase of the authentication process is found at the biometric sensor level. There are well-known attacks such as the presentation attack [61], also known as the spoofing attack which involves various misleading means such as photographic paper, high-resolution photos, video footage, or authentication masks (2D, 3D), the morphing attack [62] which consists of creating artificial facial biometric samples by integrating several faces into a single face, and Deepfake technologies based on artificial intelligence algorithms and deep learning which aim to create fake digital images as persuasive as possible or videos that achieve similarity between two users.  
Another vulnerable point of the mechanism is the biometric database, which can be hacked, either by coercion of the system administrator, by completely bypassing the security rings around the database, or by an external attacker who can steal the administrator privileges and the access credentials to directly modify the data stored in the database. Various information can also be injected into the database that can compromise it or alter the biometric templates so that the system can no longer function at optimal parameters.  
New attack vulnerabilities can also be identified on the information transmission channel by manipulating the information content, the MAC address (ARP spoofing), or the Wi-Fi signal (honeypots) to impersonate the system users, but also exploiting it through various cyberattacks such as DoS (Denial of Service), malware, phishing, and Advanced Persistent Threat (APT).  
An attacker can escalate all of the presented vulnerabilities and perform various attacks to compromise the security of the authentication system, such as accessing private information, obtaining elevated authorization permissions, manipulating access control systems, or accessing unauthorized facilities. As a result, there is an urgent need to implement additional measures to increase the accuracy and performance of facial recognition systems, including the implementation of motion-based features, image quality-based features, adaptive authentication, capitalizing on deep-learning and machine learning algorithms, and developing multimodal systems and cryptographic algorithms.  
Making a comparative analysis of these defensive techniques based on cost versus security factors, the techniques that use additional hardware involve additional costs but have a better level of robustness and security compared to software techniques that have lower costs but ensure relatively lower security.  
From this perspective, for this research, we selected for implementation the hardware defensive techniques that execute user authentication in different real situations, independently of the host computer to limit the extension of security breaches.