Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis

• URL: http://arxiv.org/abs/2306.11710v2
• Date: Thu, 22 Jun 2023 10:15:48 GMT
• Title: Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis
• Authors: Maxim Maximov, Tim Meinhardt, Ismail Elezi, Zoe Papakipos, Caner Hazirbas, Cristian Canton Ferrer, Laura Leal-Taix\'e
• Abstract summary: We motivate and introduce the Pedestrian dataset De-Identification task. PDI evaluates the degree of de-identification and downstream task training performance for a given de-identification method. We show how our data is able to narrow the synthetic-to-real performance gap in a privacy-conscious manner.
• Score: 16.394031759681678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advent of data-driven technology solutions is accompanied by an increasing concern with data privacy. This is of particular importance for human-centered image recognition tasks, such as pedestrian detection, re-identification, and tracking. To highlight the importance of privacy issues and motivate future research, we motivate and introduce the Pedestrian Dataset De-Identification (PDI) task. PDI evaluates the degree of de-identification and downstream task training performance for a given de-identification method. As a first baseline, we propose IncogniMOT, a two-stage full-body de-identification pipeline based on image synthesis via generative adversarial networks. The first stage replaces target pedestrians with synthetic identities. To improve downstream task performance, we then apply stage two, which blends and adapts the synthetic image parts into the data. To demonstrate the effectiveness of IncogniMOT, we generate a fully de-identified version of the MOT17 pedestrian tracking dataset and analyze its application as training data for pedestrian re-identification, detection, and tracking models. Furthermore, we show how our data is able to narrow the synthetic-to-real performance gap in a privacy-conscious manner.

Related papers

• SDFR: Synthetic Data for Face Recognition Competition [51.9134406629509]
  Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. Recently several works proposed generating synthetic face recognition datasets to mitigate concerns in web-crawled face recognition datasets. This paper presents the summary of the Synthetic Data for Face Recognition (SDFR) Competition held in conjunction with the 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024) The SDFR competition was split into two tasks, allowing participants to train face recognition systems using new synthetic datasets and/or existing ones.
  arXiv  Detail & Related papers  (2024-04-06T10:30:31Z)
• If It's Not Enough, Make It So: Reducing Authentic Data Demand in Face Recognition through Synthetic Faces [16.977459035497162]
  Large face datasets are primarily sourced from web-based images, lacking explicit user consent. In this paper, we examine whether and how synthetic face data can be used to train effective face recognition models.
  arXiv  Detail & Related papers  (2024-04-04T15:45:25Z)
• Contrastive Transformer Learning with Proximity Data Generation for Text-Based Person Search [60.626459715780605]
  Given a descriptive text query, text-based person search aims to retrieve the best-matched target person from an image gallery. Such a cross-modal retrieval task is quite challenging due to significant modality gap, fine-grained differences and insufficiency of annotated data. In this paper, we propose a simple yet effective dual Transformer model for text-based person search.
  arXiv  Detail & Related papers  (2023-11-15T16:26:49Z)
• 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)
• Learning Cross-modality Information Bottleneck Representation for Heterogeneous Person Re-Identification [61.49219876388174]
  Visible-Infrared person re-identification (VI-ReID) is an important and challenging task in intelligent video surveillance. Existing methods mainly focus on learning a shared feature space to reduce the modality discrepancy between visible and infrared modalities. We present a novel mutual information and modality consensus network, namely CMInfoNet, to extract modality-invariant identity features.
  arXiv  Detail & Related papers  (2023-08-29T06:55:42Z)
• Disguise without Disruption: Utility-Preserving Face De-Identification [40.484745636190034]
  We introduce Disguise, a novel algorithm that seamlessly de-identifies facial images while ensuring the usability of the modified data. Our method involves extracting and substituting depicted identities with synthetic ones, generated using variational mechanisms to maximize obfuscation and non-invertibility. We extensively evaluate our method using multiple datasets, demonstrating a higher de-identification rate and superior consistency compared to prior approaches in various downstream tasks.
  arXiv  Detail & Related papers  (2023-03-23T13:50:46Z)
• Attribute-preserving Face Dataset Anonymization via Latent Code Optimization [64.4569739006591]
  We present a task-agnostic anonymization procedure that directly optimize the images' latent representation in the latent space of a pre-trained GAN. We demonstrate through a series of experiments that our method is capable of anonymizing the identity of the images whilst -- crucially -- better-preserving the facial attributes.
  arXiv  Detail & Related papers  (2023-03-20T17:34:05Z)
• Decouple-and-Sample: Protecting sensitive information in task agnostic data release [17.398889291769986]
  sanitizer is a framework for secure and task-agnostic data release. We show that a better privacy-utility trade-off is achieved if sensitive information can be synthesized privately.
  arXiv  Detail & Related papers  (2022-03-17T19:15:33Z)
• AI-based Re-identification of Behavioral Clickstream Data [0.0]
  This paper demonstrates that similar techniques can be applied to successfully re-identify individuals purely based on their behavioral patterns. The mere resemblance of behavioral patterns between records is sufficient to correctly attribute behavioral data to identified individuals. We also demonstrate how synthetic data can offer a viable alternative, that is shown to be resilient against our introduced AI-based re-identification attacks.
  arXiv  Detail & Related papers  (2022-01-21T16:49:00Z)
• Unsupervised Domain Adaptive Learning via Synthetic Data for Person Re-identification [101.1886788396803]
  Person re-identification (re-ID) has gained more and more attention due to its widespread applications in video surveillance. Unfortunately, the mainstream deep learning methods still need a large quantity of labeled data to train models. In this paper, we develop a data collector to automatically generate synthetic re-ID samples in a computer game, and construct a data labeler to simultaneously annotate them.
  arXiv  Detail & Related papers  (2021-09-12T15:51:41Z)
• The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection, Tracking, Re-Identification and Search from Aerial Devices [7.095987222706225]
  This paper introduces the P-DESTRE dataset, which is the first of its kind to provide consistent ID annotations across multiple days. We also compare the results attained by state-of-the-art pedestrian detection, tracking, reidentification and search techniques in well-known surveillance datasets. The dataset and the full details of the empirical evaluation carried out are freely available at http://p-destre.di.ubi.pt/.
  arXiv  Detail & Related papers  (2020-04-06T16:17:32Z)

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.