Personalized Privacy Protection Mask Against Unauthorized Facial Recognition

• URL: http://arxiv.org/abs/2407.13975v1
• Date: Fri, 19 Jul 2024 01:59:00 GMT
• Title: Personalized Privacy Protection Mask Against Unauthorized Facial Recognition
• Authors: Ka-Ho Chow, Sihao Hu, Tiansheng Huang, Ling Liu,
• Abstract summary: Face recognition (FR) can be abused for privacy intrusion. Governments, private companies, or even individual attackers can collect facial images by web scraping to build an FR system identifying human faces without their consent. This paper introduces Chameleon, which learns to generate a user-centric personalized privacy protection mask, coined as P3-Mask, to protect facial images against unauthorized FR with three salient features.
• Score: 11.078375461923088
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face recognition (FR) can be abused for privacy intrusion. Governments, private companies, or even individual attackers can collect facial images by web scraping to build an FR system identifying human faces without their consent. This paper introduces Chameleon, which learns to generate a user-centric personalized privacy protection mask, coined as P3-Mask, to protect facial images against unauthorized FR with three salient features. First, we use a cross-image optimization to generate one P3-Mask for each user instead of tailoring facial perturbation for each facial image of a user. It enables efficient and instant protection even for users with limited computing resources. Second, we incorporate a perceptibility optimization to preserve the visual quality of the protected facial images. Third, we strengthen the robustness of P3-Mask against unknown FR models by integrating focal diversity-optimized ensemble learning into the mask generation process. Extensive experiments on two benchmark datasets show that Chameleon outperforms three state-of-the-art methods with instant protection and minimal degradation of image quality. Furthermore, Chameleon enables cost-effective FR authorization using the P3-Mask as a personalized de-obfuscation key, and it demonstrates high resilience against adaptive adversaries.

Related papers

• Diff-Privacy: Diffusion-based Face Privacy Protection [58.1021066224765]
  In this paper, we propose a novel face privacy protection method based on diffusion models, dubbed Diff-Privacy. Specifically, we train our proposed multi-scale image inversion module (MSI) to obtain a set of SDM format conditional embeddings of the original image. Based on the conditional embeddings, we design corresponding embedding scheduling strategies and construct different energy functions during the denoising process to achieve anonymization and visual identity information hiding.
  arXiv  Detail & Related papers  (2023-09-11T09:26:07Z)
• PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via Secure Flow [69.78820726573935]
  We name it PRO-Face S, short for Privacy-preserving Reversible Obfuscation of Face images via Secure flow-based model. In the framework, an Invertible Neural Network (INN) is utilized to process the input image along with its pre-obfuscated form, and generate the privacy protected image that visually approximates to the pre-obfuscated one.
  arXiv  Detail & Related papers  (2023-07-18T10:55:54Z)
• CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search [10.16904417057085]
  Deep learning based face recognition systems can enable unauthorized tracking of users in the digital world. Existing methods for enhancing privacy fail to generate naturalistic images that can protect facial privacy without compromising user experience. We propose a novel two-step approach for facial privacy protection that relies on finding adversarial latent codes in the low-dimensional manifold of a pretrained generative model.
  arXiv  Detail & Related papers  (2023-06-16T17:58:15Z)
• Attribute-Guided Encryption with Facial Texture Masking [64.77548539959501]
  We propose Attribute Guided Encryption with Facial Texture Masking to protect users from unauthorized facial recognition systems. Our proposed method produces more natural-looking encrypted images than state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T23:50:43Z)
• Privacy-preserving Adversarial Facial Features [31.885215405010687]
  We propose an adversarial features-based face privacy protection approach to generate privacy-preserving adversarial features. We show that AdvFace outperforms the state-of-the-art face privacy-preserving methods in defending against reconstruction attacks.
  arXiv  Detail & Related papers  (2023-05-08T08:52:08Z)
• OPOM: Customized Invisible Cloak towards Face Privacy Protection [58.07786010689529]
  We investigate the face privacy protection from a technology standpoint based on a new type of customized cloak. We propose a new method, named one person one mask (OPOM), to generate person-specific (class-wise) universal masks. The effectiveness of the proposed method is evaluated on both common and celebrity datasets.
  arXiv  Detail & Related papers  (2022-05-24T11:29:37Z)
• FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders [81.21440457805932]
  We propose a novel framework FaceMAE, where the face privacy and recognition performance are considered simultaneously. randomly masked face images are used to train the reconstruction module in FaceMAE. We also perform sufficient privacy-preserving face recognition on several public face datasets.
  arXiv  Detail & Related papers  (2022-05-23T07:19:42Z)
• Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-robust Makeup Transfer [24.25863892897547]
  adversarial makeup transfer GAN (AMT-GAN) is a novel face protection method aiming at constructing adversarial face images. In this paper, we introduce a new regularization module along with a joint training strategy to reconcile the conflicts between the adversarial noises and the cycle consistence loss in makeup transfer.
  arXiv  Detail & Related papers  (2022-03-07T03:56:17Z)
• Towards Face Encryption by Generating Adversarial Identity Masks [53.82211571716117]
  We propose a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks. TIP-IM provides 95%+ protection success rate against various state-of-the-art face recognition models.
  arXiv  Detail & Related papers  (2020-03-15T12:45:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.