# Novel Deep Learning-Based Facial Forgery Detection for Effective Biometric Recognition
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Methods** & **Advantages** & **Weaknesses** \\ \hline \multirow{4}{*}{Physiological} & Fingerprint & High resolution low price & Easy to duplicate \\  & Face & Contactless low price & Sensitive to lighting weak at disguise \\  & Iris & Not duplicable high resolution & Inconvenient to use high price \\  & Vein & Contactless & high resolution High price \\  & Palmprint & Easy to use & Low resolution high price \\ \hline \multirow{2}{*}{Behavioral} & Signature & Easy to use low price & Low resolution easy to duplicate \\  & Voiceprint & Contactless low price & Sensitive to human variation low resolution \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advantages and weaknesses of selected biometrics. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Types** & **Techniques** & **Use Cases** & **Analysis** \\ \hline \multirow{4}{*}{Deepfake Attacks} & Face swapping, expression manipulation, lip sync manipulation, etc. & Fraud, malicious information dissemination, identity theft, etc. & developing. Various techniques are used, including deep learning-based detection models, facial feature analysis, and biometric signal analysis. Therefore, the verification of the authenticity of biometric information has become a critical issue. In alignment with the utilization and advancement of facial recognition technology, various facial recognition forgery techniques have emerged, involving manipulated photos, digital reproductions on smart devices, 3D masks, and cosmetic makeup [4; 10]. Anti-spoofing refers to technologies that detect such forgeries to ascertain the authenticity of facial features. Traditional methods, utilizing techniques like Local Binary Patterns (LBP) or Histogram of Oriented Gradients (HOG), focus on detecting alterations in images. However, these methods are often limited to specific forgery types or specific scenarios, lacking adaptability to newly discovered forgery methods. Additionally, research is actively addressing the detection of human-specific features, such as eye blinking and subtle movements, which are characteristic of genuine individuals [11]. Nevertheless, these methods may require several seconds of continuous video capture and may be vulnerable to video replay attacks. While significant progress has been made in the field of biometric facial recognition, challenges persist in developing comprehensive anti-spoofing measures that can adapt to evolving forgery techniques and effectively address real-world scenarios. Over the years, advancements in fields such as mathematics, mechanics, and cognitive science have paved the way for the continuous development of various image processing and computer vision algorithms [12]. In particular, recent breakthroughs in deep learning technology have significantly benefited the domain of image processing, witnessing diverse applications. Notably, convolutional neural networks (CNNs) have emerged as one of the most successful models in deep learning, particularly excelling in object recognition and decision-making tasks [13]. This integration of deep learning and computer vision, providing machines with visual perception, offers promising solutions to long-standing problems that humanity has sought to address [14]. Additionally, research continues on deep learning approaches to identify forged features. One notable example of outstanding research is the development of an algorithm that efficiently distinguishes facial forgery by self-learning genuine faces [15]. Additionally, an algorithm is proposed that efficiently distinguishes facial forgery by adjustably learning the weights of the 3 \(\times\) 3 gradient filter used in convolution [16]. Furthermore, there have been significant advances in face recognition due to deep learning techniques [3]. However, research on facial forgery prevention is still largely focused on addressing known forgery techniques, while studies on effectively responding to emerging forgery techniques remain limited.