Visual Coherence Face Anonymization Algorithm Based on Dynamic Identity Perception

In the era of the meta-universe and the proliferation of personalized social networks, interactive behaviors like sharing personal and family photos pose an escalating risk of privacy breaches and identity exposure. A potential remedy lies in substituting real images with anonymized face images in public contexts. While existing face anonymization methods often replace substantial portions of face images, the resultant faces lack sufficient similarity to the originals. To address this, we propose a anonymization model leveraging saliency analysis to detect identity relevant facial region, preserving visual coherence and avoiding recognition by face recognition systems. Our model comprises two integral networks: the Dynamic Identity Perception Network (DIPNet) and the improved PSPNet. DIPNet, in particular, encompasses two vital sub-modules: the dynamic region perception module detects identity relevant region; the anonymization region control module governs the size of region through thresholding, thereby dominating the preservation of identity independent features and the degree of anonymization. The improved PSPNet produces high-quality identity anonymized faces. Experimental results demonstrate that our method yields realistic anonymized faces, retaining original features and deceiving face recognition systems, safeguarding privacy in the modern digital landscape.