Enhanced DeepFake Detection Using CNN and EfficientNet-Based Ensemble Models for Robust Facial Manipulation Analysis

Deepfakes are generally powered by Generative adversarial networks (GANs) that has been newly introduced a new dimension of realism in the manipulation of digital media particularly in some delicate features such as eyes, lips, and skin texture. These sophisticated manipulations pose significant threats to digital security, privacy, and trust in media. Detecting these alterations, especially in still images, has become a critical focus of research. This paper presents a Convolutional neural network (CNN)-based approach to detecting facial feature manip- ulations in deepfake images. Leveraging CNN architectures like ResNet, we focus on identifying subtle pixel-level inconsistencies in facial regions. Preprocessing steps, including face cropping, alignment, and normalization, are applied to enhance the model's focus on manipulated features. Datasets such as Face Forensics++ are utilized for training and evaluation. Our model demonstrates a high level of accuracy in distinguishing real from fake images based on facial feature manipulations, outperforming baseline models in several key metrics, including precision and recall. The results highlight the effectiveness of CNNs in detecting fine- grained alterations and their potential for real-world application in combating deepfake threats.This research contributes to the ongoing efforts in deepfake detection by proposing a robust and scalable method to detect facial manipulations, ultimately enhancing the security and authenticity of digital media. Future work will explore the integration of multimodal detection tech- niques to further improve the robustness of the system.