# Novel Deep Learning-Based Facial Forgery Detection for Effective Biometric Recognition
## 1 Introduction
Since malicious events are relatively rare compared to regular network activity, this data imbalance can hinder a security system's ability to accurately detect attacks, leaving the system vulnerable to undetected threats. 4. Law Enforcement and Forensics: Data imbalance also exists in fields such as crime pattern detection and forensic analysis. For example, rare events such as serial killings or specific criminal behaviors (e.g., anonymous threat letters) are difficult to model due to their infrequency. This scarcity of data makes it hard for predictive models to identify or generalize patterns in such cases, which can have significant real-world implications, such as delays in identifying criminal activity. To address the issue of data imbalance, various methods have been proposed, each with its own set of advantages and disadvantages [18]. One of the most common approaches is _oversampling_, where additional samples are generated for the minority class to balance the dataset. This can be achieved by duplicating existing samples or through techniques like data augmentation, where synthetic samples are created by slightly altering existing data points. Image augmentation methods include random shifts, flips, rotations, adjustments of brightness, and combinations of these to the designated image data. While oversampling is simple to implement, it can increase the risk of _overfitting_, especially if duplicate data leads the model to \"memorize\" specific patterns in the minority class rather than learning to generalize. Another approach is _undersampling_, which involves reducing the number of samples in the majority class to create a more balanced dataset. This can be conducted randomly or using algorithms that selectively remove redundant data. Undersampling reduces the likelihood of overfitting but comes with the disadvantage of potentially discarding valuable information from the majority class, which may negatively affect the model's overall performance. Addressing data imbalance is critical for ensuring that machine learning models perform well, especially in applications where the accurate detection of rare events is vital. Choosing the right technique for handling data imbalance depends on the specific problem, the available data, and the performance requirements of the model. Both oversampling and undersampling have their place, but careful consideration must be given to avoid overfitting or the loss of important data. As machine learning continues to be applied in high-stakes domains such as healthcare, finance, and cybersecurity, finding effective solutions to the data imbalance problem remains an essential challenge for researchers and practitioners alike. In this study, a novel facial forgery detection technique is proposed for effective biometric recognition using innovative deep learning networks and algorithms. The proposed algorithm effectively addresses the chronic issue of data imbalance in deep learning. Additionally, the approach allows for the determination of forgery authenticity without being constrained by specific forgery methods. The implementation and experimental results demonstrate an ACER of approximately 0.06, achieving a maximum performance improvement compared to previous studies. The rest of this paper is described as follows. In Section 2, dataset acquisition and the proposed algorithm are described. The experimental results are analyzed in Section 3, and the overall discussion is mentioned in Section 4. The terms \"forgery\", \"spoofing\", \"counterfeiting\", and \"manipulation\" can have different definitions depending on the context, both in general and technical manner. In this paper, the term \"forgery\" is used to encompass all of the meanings of the terms mentioned above, as they all involve the creation of false or counterfeit facial information. Also, the term \"genuine\" is used to refer to original, unaltered facial information. This clarification of terms will help to avoid any confusion and ensure that the results of this study are interpreted correctly.