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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 verificationenabled 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.
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 verificationenabled 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.
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