Adversarial Secret-Identity Generation Model for Face Anonymization in the Internet of Vehicles

With the rapid development of the Internet of Vehicles, the systematical cameras in vehicles can easily collect mass personal face data, which is very private on the Internet. This article focuses on solving the face protection problem caused by the Internet of Vehicles. Previous studies have proposed various face protection models based on obfuscation, neural networks, or adversarial techniques to prevent the disclosure of identity information. Even with existing face anonymization algorithms, there are several issues, including weak information protection ability, not maintaining the naturalness of a protected face, and not being suitable for various emerging face recognition systems. We propose an adversarial secret-identity generation model to protect personal identities from these problems. Concretely, our model's identity encryption generative adversarial module can generate brand new faces, which ensures naturalness while making other face identification models unable to recognize the identity. In addition, our PFS module extracts the identity set that is least relevant to the inputs and most conducive to model convergence from target data and promotes the generation of excellent faces from the perspective of data. Finally, the protected identities generated by our black-box face deidentification model can resist recognizing various advanced face identification approaches. Extensive experiments prove our model can achieve a 96.5% protection success rate, achieving competitive performance with 98.1% R1-T, 99.2% R5-T, 98.5% R1-U, and 97.1% R5-U against ArcFace and beating out various superior face anonymization algorithms. Meanwhile, we introduce a model compression algorithm to reduce the network scale of this model, demonstrating the scalability of various intelligent vehicles.