A Method for Quantitative Object De-Identification Analysis of Anonymized Video

The rise in video content has amplified the risk of information leakage, prompting the development of various anonymization techniques. These techniques include traditional methods such as pixelation, blurring, and masking, as well as more advanced approaches like video encryption, which involves encrypting parameters during video encoding, and techniques that subtly alter facial features to prevent identification. However, assessing the effectiveness of these anonymization techniques remains challenging. Common metrics such as the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR), often used for object anonymization in videos, primarily assess image quality and lack accuracy for security evaluation. Furthermore, subjective human evaluations fail to provide the consistent, quantitative data necessary for cross-study comparisons. Given that a single identifiable frame in a video can compromise overall security, this study introduces a quantitative method for evaluating the de-identification rate in regions of interest (ROI) within videos. The proposed approach calculates an object de-identification rate (ODR) by combining SSIM and edge detection ratio (EDR) using a harmonic mean and a power mean. Our proposed method enhances the precision and reliability for security of anonymized video.