FAKER: Full-body Anonymization with Human Keypoint Extraction for Real-time Video Deidentification

• URL: http://arxiv.org/abs/2408.11829v1
• Date: Tue, 6 Aug 2024 04:59:23 GMT
• Title: FAKER: Full-body Anonymization with Human Keypoint Extraction for Real-time Video Deidentification
• Authors: Byunghyun Ban, Hyoseok Lee,
• Abstract summary: We propose a novel approach that employs a significantly smaller model to achieve real-time full-body anonymization of individuals in videos. By leveraging pose estimation algorithms, our approach accurately represents information regarding individuals' positions, movements, and postures. This algorithm can be seamlessly integrated into CCTV or IP camera systems installed in various industrial settings.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In the contemporary digital era, protection of personal information has become a paramount issue. The exponential growth of the media industry has heightened concerns regarding the anonymization of individuals captured in video footage. Traditional methods, such as blurring or pixelation, are commonly employed, while recent advancements have introduced generative adversarial networks (GAN) to redraw faces in videos. In this study, we propose a novel approach that employs a significantly smaller model to achieve real-time full-body anonymization of individuals in videos. Unlike conventional techniques that often fail to effectively remove personal identification information such as skin color, clothing, accessories, and body shape while our method successfully eradicates all such details. Furthermore, by leveraging pose estimation algorithms, our approach accurately represents information regarding individuals' positions, movements, and postures. This algorithm can be seamlessly integrated into CCTV or IP camera systems installed in various industrial settings, functioning in real-time and thus facilitating the widespread adoption of full-body anonymization technology.

Related papers

• Context-Aware Full Body Anonymization using Text-to-Image Diffusion Models [1.5088726951324294]
  Anonymization plays a key role in protecting sensible information of individuals in real world datasets. In this paper, we propose a workflow for full body person anonymization utilizing Stable Diffusion as a generative backend. We show that our method outperforms state-of-the art anonymization pipelines with respect to image quality, resolution, Inception Score (IS) and Frechet Inception Distance (FID)
  arXiv  Detail & Related papers  (2024-10-11T06:04:30Z)
• CausalVE: Face Video Privacy Encryption via Causal Video Prediction [13.577971999457164]
  With the proliferation of video and live-streaming websites, public-face video distribution and interactions pose greater privacy risks. We propose a neural network framework, CausalVE, to address these shortcomings. Our framework has good security in public video dissemination and outperforms state-of-the-art methods from a qualitative, quantitative, and visual point of view.
  arXiv  Detail & Related papers  (2024-09-28T10:34:22Z)
• ID-Guard: A Universal Framework for Combating Facial Manipulation via Breaking Identification [60.73617868629575]
  misuse of deep learning-based facial manipulation poses a potential threat to civil rights. To prevent this fraud at its source, proactive defense technology was proposed to disrupt the manipulation process. We propose a novel universal framework for combating facial manipulation, called ID-Guard.
  arXiv  Detail & Related papers  (2024-09-20T09:30:08Z)
• 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)
• 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 Forgery Detection Based on Facial Region Displacement Trajectory Series [10.338298543908339]
  We develop a method for detecting manipulated videos based on the trajectory of the facial region displacement. This information was used to construct a network for exposing multidimensional artifacts in the trajectory sequences of manipulated videos.
  arXiv  Detail & Related papers  (2022-12-07T14:47:54Z)
• 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)
• Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes [131.9067467127761]
  We focus on improving the action recognition by fully-utilizing the information of scenes and collecting new data. Specifically, we adopt a strong human detector to detect spatial location of each frame. We then apply action recognition models to learn thetemporal information from video frames on both the HIE dataset and new data with diverse scenes from the internet.
  arXiv  Detail & Related papers  (2020-10-16T13:08:50Z)
• CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks [12.20367903755194]
  CIAGAN is a model for image and video anonymization based on conditional generative adversarial networks. Our model is able to remove the identifying characteristics of faces and bodies while producing high-quality images and videos.
  arXiv  Detail & Related papers  (2020-05-19T15:56:08Z)
• 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.