Visual Privacy Protection Method Based on YOLOv8 and ControlNet

To achieve a balance between visual privacy protection and visual information integrity, we propose a visual privacy protection method based on YOLOv8 and ControlNet. In the privacy recognition process, we realize the targeted selection of redrawing area by recognizing privacy features and effectively segmenting the people and backgrounds that leak privacy. In the privacy preservation process, individuals undergo virtual dressing through the ControlNet-guided Stable Diffusion model, resulting in visually protected images. Empirical studies demonstrate promising results, with an average accuracy of $94.7 \%$ in privacy identification. Additionally, the average Structural Similarity Index (SSIM) value between privacy-protected images and original images stands at 0.843