Privacy-Preserving Face Recognition Using Random Frequency Components
- URL: http://arxiv.org/abs/2308.10461v1
- Date: Mon, 21 Aug 2023 04:31:02 GMT
- Title: Privacy-Preserving Face Recognition Using Random Frequency Components
- Authors: Yuxi Mi, Yuge Huang, Jiazhen Ji, Minyi Zhao, Jiaxiang Wu, Xingkun Xu,
Shouhong Ding, Shuigeng Zhou
- Abstract summary: Face recognition has sparked increasing privacy concerns.
We propose to conceal visual information by pruning human-perceivable low-frequency components.
We distill our findings into a novel privacy-preserving face recognition method, PartialFace.
- Score: 46.95003101593304
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The ubiquitous use of face recognition has sparked increasing privacy
concerns, as unauthorized access to sensitive face images could compromise the
information of individuals. This paper presents an in-depth study of the
privacy protection of face images' visual information and against recovery.
Drawing on the perceptual disparity between humans and models, we propose to
conceal visual information by pruning human-perceivable low-frequency
components. For impeding recovery, we first elucidate the seeming paradox
between reducing model-exploitable information and retaining high recognition
accuracy. Based on recent theoretical insights and our observation on model
attention, we propose a solution to the dilemma, by advocating for the training
and inference of recognition models on randomly selected frequency components.
We distill our findings into a novel privacy-preserving face recognition
method, PartialFace. Extensive experiments demonstrate that PartialFace
effectively balances privacy protection goals and recognition accuracy. Code is
available at: https://github.com/Tencent/TFace.
Related papers
- Privacy-preserving Optics for Enhancing Protection in Face De-identification [60.110274007388135]
We propose a hardware-level face de-identification method to solve this vulnerability.
We also propose an anonymization framework that generates a new face using the privacy-preserving image, face heatmap, and a reference face image from a public dataset as input.
arXiv Detail & Related papers (2024-03-31T19:28:04Z) - Privacy-Preserving Face Recognition Using Trainable Feature Subtraction [40.47645421424354]
Face recognition has led to increasing privacy concerns.
This paper explores face image protection against viewing and recovery attacks.
We distill our methodologies into a novel privacy-preserving face recognition method, MinusFace.
arXiv Detail & Related papers (2024-03-19T05:27:52Z) - Privacy-Preserving Face Recognition in Hybrid Frequency-Color Domain [16.05230409730324]
Face image is a sensitive biometric attribute tied to the identity information of each user.
This paper proposes a hybrid frequency-color fusion approach to reduce the input dimensionality of face recognition.
It has around 2.6% to 4.2% higher accuracy than the state-of-the-art in the 1:N verification scenario.
arXiv Detail & Related papers (2024-01-24T11:27:32Z) - 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) - Face Encryption via Frequency-Restricted Identity-Agnostic Attacks [25.198662208981467]
Malicious collectors use deep face recognition systems to easily steal biometric information.
We propose a frequency-restricted identity-agnostic (FRIA) framework to encrypt face images from unauthorized face recognition.
arXiv Detail & Related papers (2023-08-11T07:38:46Z) - Enhancing Mobile Privacy and Security: A Face Skin Patch-Based
Anti-Spoofing Approach [0.0]
Face anti-spoofing system(FAS) for face recognition is an important component used to enhance the security of face recognition systems.
Traditional FAS used images containing identity information to detect spoofing traces, however there is a risk of privacy leakage during the transmission and storage of these images.
We propose a face anti-spoofing algorithm based on facial skin patches leveraging pure facial skin patch images as input.
arXiv Detail & Related papers (2023-08-09T08:36:13Z) - DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel
Splitting in the Frequency Domain [23.4606547767188]
DuetFace is a privacy-preserving face recognition method that employs collaborative inference in the frequency domain.
The proposed method achieves a comparable recognition accuracy and cost to the unprotected ArcFace and outperforms the state-of-the-art privacy-preserving methods.
arXiv Detail & Related papers (2022-07-15T08:35:44Z) - Privacy-Preserving Face Recognition with Learnable Privacy Budgets in
Frequency Domain [77.8858706250075]
This paper proposes a privacy-preserving face recognition method using differential privacy in the frequency domain.
Our method performs very well with several classical face recognition test sets.
arXiv Detail & Related papers (2022-07-15T07:15:36Z) - 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) - Investigating the Impact of Inclusion in Face Recognition Training Data
on Individual Face Identification [93.5538147928669]
We audit ArcFace, a state-of-the-art, open source face recognition system, in a large-scale face identification experiment with more than one million distractor images.
We find a Rank-1 face identification accuracy of 79.71% for individuals present in the model's training data and an accuracy of 75.73% for those not present.
arXiv Detail & Related papers (2020-01-09T15:50:28Z)
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.