# Novel Deep Learning-Based Facial Forgery Detection for Effective Biometric Recognition
## 1 Introduction  
With the continuous development of science, technology, and computer engineering, many traditional techniques in society are rapidly transitioning to methods utilizing digital technology. The methods for personal identification and authentication are no exception. Biometric recognition technology offers a compelling solution by leveraging an individual's unique physiological or behavioral traits for identification and verification. Biometric recognition technology, providing a secure, accurate, fast, and convenient means of distinguishing and verifying individuals, has undergone significant advancements across academic research and various industries in response to societal needs and interests. It is estimated that the global biometrics market will grow to 86.1 billion USD by 2028 from its projected 2023 value of 47.8 billion USD [1], showcasing the increasing influence of digital technology in everyday life. Biometric recognition technology can be classified into _physiological features_, which directly extract information from the body, and _behavioral features_, which measure specific individual behavior patterns. Examples of physiological features include fingerprints, faces, irises, retinas, veins, hand geometry, palm prints, and brainwaves, while behavioral features encompass signatures, voiceprints, gait, and keystroke dynamics. Key methods and characteristics of the commonly used biometrics are described in Table 1[2]. As biometric recognition technology continues to evolve, its security and convenience are expected to improve further. However, addressing privacy concerns and ensuring ethical practices are crucial for its widespread adoption. By striking a balance between innovation and responsibility, biometric authentication has the potential to revolutionize personal identification, enhancing security and convenience across various industries and aspects of daily life. Among these biometric recognition and human identification technologies, face is the most common characteristic used by humans for recognition [3]. Facial recognition technology, due to its simplicity, convenience, and widespread use of high-resolution smart devices, has gained considerable attention in daily life [4]. With the increasing deployment of face recognition in many applications in the field of the cyber physical system and the industrial control system such as remote-sensing entrance guards, surveillance systems, and intelligent human machine interfaces, its security concern becomes increasingly important [5]. Concurrently, the development of computing technology, advancements in personal computers and digital camera capabilities, the emergence of high-performance image processing hardware and software, and the revolutionary progress of 3D printing have empowered individuals to easily create, modify, and manipulate various types of information. Therefore, facial information can also be easily and sophisticatedly forged. Forgery, also known as spoofing, counterfeiting, or manipulation, involves intentionally altering or creating false information with deceptive intent. The use of forged information can lead to the irreparable loss of property and trust, necessitating significant capital, time, and effort for recovery [6]. The relentless march of technological progress has brought about a fascinating paradox. While computing power, PC and digital camera capabilities, and high-performance image processing tools have empowered individuals for legitimate purposes, they have also inadvertently opened a Pandora's box of manipulation. This very development has made facial information, once considered a reliable identifier, susceptible to sophisticated forgery. Significant facial forgery attack types with their characteristics are described in Table 2[7; 8; 9].