Deepfake Detection Using EfficientNetB7: Efficacy, Efficiency, and Adaptability - Vineet Kumar, Mustansir Godhrawala, Shubham Kolaskar, Yash Nagare, Pratham Shah, Jayan Shah, Nilakshi Jain, Shwetambari Borade, Bhavesh Patel (International Journal of Intelligent Systems and Applications in Engineering, 2024)
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.