Nowadays,the proliferation of portraits or photographs containing human faces on the internet has created significant risks of illegal privacy collection and analysis by intelligent systems.Previous attempts to protec...Nowadays,the proliferation of portraits or photographs containing human faces on the internet has created significant risks of illegal privacy collection and analysis by intelligent systems.Previous attempts to protect against unauthorized identification by face recognition models have primarily involved manipulating or adding adversarial perturbations to photos.However,it remains a challenge to balance privacy protection effectiveness and maintaining image visual quality.That is,to successfully attack real-world black-box face recognition models,significant manipulation is required for the source image,which will obviously damage the image visual quality.To address these issues,we propose an attribute-guided face identity protection(AG-FIP)approach that can protect facial privacy effectively without introducing meaningless or conspicuous artifacts into the source image.The proposed method involves mapping the images to latent space and subsequently implementing an adversarial attack through attribute editing.An attribute selection module followed by an attribute adversarially editing module is proposed to enhance the efficiency and effectiveness of adversarial attacks.Experimental results demonstrate that our approach outperforms SOTAs in terms of confusing black-box face recognition models,commercial face recognition APIs,and image visual quality.展开更多
文摘Nowadays,the proliferation of portraits or photographs containing human faces on the internet has created significant risks of illegal privacy collection and analysis by intelligent systems.Previous attempts to protect against unauthorized identification by face recognition models have primarily involved manipulating or adding adversarial perturbations to photos.However,it remains a challenge to balance privacy protection effectiveness and maintaining image visual quality.That is,to successfully attack real-world black-box face recognition models,significant manipulation is required for the source image,which will obviously damage the image visual quality.To address these issues,we propose an attribute-guided face identity protection(AG-FIP)approach that can protect facial privacy effectively without introducing meaningless or conspicuous artifacts into the source image.The proposed method involves mapping the images to latent space and subsequently implementing an adversarial attack through attribute editing.An attribute selection module followed by an attribute adversarially editing module is proposed to enhance the efficiency and effectiveness of adversarial attacks.Experimental results demonstrate that our approach outperforms SOTAs in terms of confusing black-box face recognition models,commercial face recognition APIs,and image visual quality.
