Verifiable Facial De-identification in Video Surveillance

With the advancement of facial recognition technology, concerns over facial privacy breaches owing to data leaks and external attacks have been escalating. Existing de-identification methods face challenges with compatibility with facial recognition models and difficulties in verifying de-identified images. To address these issues, this study introduces a novel framework that combines face verification-enabled de-identification techniques with face-swapping methods, tailored for video surveillance environments. This framework employs StyleGAN, Pixel2Style2Pixel (PSP), HopSkipJumpAttack (HSJA), and FaceNet512 to achieve face verification-capable de-identification, and uses the dlib library for face swapping. Experimental results demonstrate that this method maintains high face recognition performance (98.37%) across various facial recognition models while achieving effective de-identification. Additionally, human tests have validated its sufficient de-identification capabilities, and image quality assessments have shown its excellence across various metrics. Moreover, real-time de-identification feasibility was evaluated using Nvidia Jetson AGX Xavier, achieving a processing speed of up to 9.68 fps. These results mark a significant advancement in demonstrating the practicality of high-quality de-identification techniques and facial privacy protection in the field of video surveillance.