ARNet: Balancing speed and accuracy in a hybrid neural network for deepfake detection

Abstract Detecting deepfakes has become one of the most pressing challenges in the field of cybersecurity and digital forensics. With the increasing use of deepfake technology for malicious purposes, such as misinformation and identity theft, there is a critical need for effective detection methods. This work introduces ARNet ( A lexNet & R esNet Net work), a hybrid deep learning model specifically designed for deepfake detection. ARNet combines the computational efficiency of AlexNet with the robust feature extraction capabilities of ResNet, addressing the limitations of traditional models. The architecture of ARNet integrates residual connections and a global pooling mechanism to improve detection accuracy while reducing computational cost and processing time. The proposed model demonstrates superior performance in both accuracy and inference speed, outperforming popular models such as AlexNet, ResNet, and MobileNet in deepfake datasets. Our results highlight the effectiveness of ARNet in both low-resource environments and large-scale deepfake detection tasks.