Enhancing Authenticity Verification with Transfer Learning and Ensemble Techniques in Facial Feature-Based Deepfake Detection

Deepfake technology, facilitated by deep learning algorithms, has emerged as a significant concern due to its potential to deceive humans with fabricated content indistinguishable from reality. The proliferation of deepfake videos presents a formidable challenge, propagating misinformation across various sectors such as social media, politics, and healthcare. Detecting and mitigating these threats is imperative for fortifying defenses and safeguarding information integrity.This paper tackles the complexities associated with deepfake detection, emphasizing the necessity for innovative approaches given the constraints of available data and the evolving nature of forgery techniques. Our proposed solution focuses on leveraging facial features and transfer learning to discern fake videos from genuine ones, aiming to identify subtle manipulations in visual content. We systematically break down videos into frames, employ the Haar cascade algorithm for facial recognition, and utilize transfer learning to extract discriminative features. We evaluate multiple pre-trained models, including VGG16, ConvNeXtTiny, EfficientNetB0, EfficientNetB7, DenseNet201, ResNet152V2, Xception, NASNetMobile, and MobileNetV2, for feature extraction. Subsequently, we feed these features into a Deep Artificial Neural Network (DANN) for deepfake detection and employ ensemble learning to combine the strengths of the best-performing models for enhanced accuracy.We found that the ensemble model comprising ConvNextTiny, EfficientNetB0, and EfficientNetB7 showed enhanced accuracy in detecting deep fakes compared to alternative models achieving up to 98% accuracy through ensemble learning.