FaceGuardian moves portrait defense into semantic space

Researchers at MBZUAI and Khalifa University introduced FaceGuardian, which optimizes subtle perturbations in a high-level semantic manifold to weaken identity recognition after diffusion editing while preserving the source image's appearance. On one dataset, face-recognition similarity fell from 0.833 for unprotected images to 0.272 with FaceGuardian, better than the next method's 0.315. After compression it scored 0.361 versus FaceLock's 0.709. The method was tested on InstructPix2Pix and LEDITS++ and won best paper at the ECCV LifeGenIP workshop. The limitation is material: researchers had access to open-model components in a gray-box setting; closed models and video remain future work.