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<p>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 (<b>A</b>lexNet &amp; <b>R</b>esNet <b>Net</b>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月21日

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  • Felipe Barreto de Oliveira,
  • Felipe de Angelis Silva,
  • Georges Daniel Amvame Nze,
  • Geraldo P. Rocha Filho,
  • André Luiz Marques Serrano,
  • Rodolfo Ipolito Meneguette,
  • Vinícius P. Gonçalves

摘要

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 (AlexNet & ResNet Network), 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.