Deepfake Detection Using EfficientNetB7: Efficacy, Efficiency, and Adaptability
Keywords:
Deepfakes, Convolutional Neural Networks, Deepfake Datasets, Efficient-NetB7, Machine LearningAbstract
This research paper explores the efficacy of Efficient-Net, a state-of-the-art Convolutional Neural Network (CNN) architecture, for the task of detecting deepfake videos. Deepfake techniques leverage advanced machine learning algorithms to generate highly realistic manipulated videos, posing a significant threat to the authenticity of visual content. Our study investigates the suitability of Efficient-Net across various scales, focusing on its ability to efficiently discern subtle visual cues indicative of deepfake manipulation. We present a comprehensive analysis of the model's performance, considering factors such as accuracy, computational efficiency, and robustness across diverse deepfake datasets. The tested accuracy of the model is around 85%. The results that we have produced do in fact correlate with the original Efficient paper, this paper proposed the accuracy of Efficient Net B7 model to be 84.4%. Experimental results demonstrate the effectiveness of Efficient-Net in mitigating the challenges posed by deepfake video detection, making it a promising candidate for deployment in real-world scenarios requiring rapid and reliable identification of manipulated visual content.
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