Original scientific paper

https://doi.org/10.17559/TV-20240801001893

GAN-Based Facial Information Protection for IoT Using Transfer Probability Models

Ye Qin ; Vocational and Technical College, Inner Mongolia Agricultural University, 010018, China
Yaowu Kang ; Vocational and Technical College, Inner Mongolia Agricultural University, 010018, China *

* Corresponding author.

Abstract

Facial information in the Internet of Things (IoT) faces great security challenges. This study proposes a novel facial information protection model combining Deep Convolutional Generative Adversarial Networks (DCGAN) with Transfer Probability Models (TPM). The model generates high-quality virtual face images while preserving key features of the original image. The results demonstrated that the model performed well in terms of encryption-decryption error (0.05), speed (632.5 Mbit/s for encryption and 583.5 Mbit/s for decryption), resource consumption (21.1%), and latency (11.5%) compared with existing methods. The model achieved more than 90% privacy protection for identity, facial expression, shape, and gesture, proving its effectiveness in facial information protection for IoT.

Keywords

deep convolutional generative adversarial network; facial features; information; protection transition probability model

Hrčak ID:

335058

URI

https://hrcak.srce.hr/335058

Publication date:

30.8.2025.

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