PPEDCRF: Privacy-Preserving Enhanced Dynamic CRF for Location-Privacy Protection for Sequence Videos with Minimal Detection Degradation

• URL: http://arxiv.org/abs/2603.01593v1
• Date: Mon, 02 Mar 2026 08:18:53 GMT
• Title: PPEDCRF: Privacy-Preserving Enhanced Dynamic CRF for Location-Privacy Protection for Sequence Videos with Minimal Detection Degradation
• Authors: Bo Ma, Jinsong Wu, Weiqi Yan, Catherine Shi, Minh Nguyen,
• Abstract summary: This paper studies location-privacy leakage under a background-based retrieval attacker.<n>It proposes PPEDCRF, a privacy-preserving conditional random field framework.<n>Experiments on public driving datasets demonstrate that PPEDCRF significantly reduces location-retrieval attack success.
• Score: 7.329775099113131
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Dashcam videos collected by autonomous or assisted-driving systems are increasingly shared for safety auditing and model improvement. Even when explicit GPS metadata are removed, an attacker can still infer the recording location by matching background visual cues (e.g., buildings and road layouts) against large-scale street-view imagery. This paper studies location-privacy leakage under a background-based retrieval attacker, and proposes PPEDCRF, a privacy-preserving enhanced dynamic conditional random field framework that injects calibrated perturbations only into inferred location-sensitive background regions while preserving foreground detection utility. PPEDCRF consists of three components: (i) a dynamic CRF that enforces temporal consistency to discover and track location sensitive regions across frames, (ii) a normalized control penalty (NCP) that allocates perturbation strength according to a hierarchical sensitivity model, and (iii) a utility-preserving noise injection module that minimizes interference to object detection and segmentation. Experiments on public driving datasets demonstrate that PPEDCRF significantly reduces location-retrieval attack success (e.g., Top-k retrieval accuracy) while maintaining competitive detection performance (e.g., mAP and segmentation metrics) compared with common baselines such as global noise, white-noise masking, and feature-based anonymization. The source code is in https://github.com/mabo1215/PPEDCRF.git

Related papers

• TIP: Resisting Gradient Inversion via Targeted Interpretable Perturbation in Federated Learning [8.156452885913108]
  Federated Learning (FL) facilitates collaborative model training while preserving data locality.<n>The exchange of gradients renders the system vulnerable to Gradient Inversion Attacks (GIAs)<n>We propose Targeted Interpretable Perturbation (TIP), a novel defense framework that integrates model interpretability with frequency domain analysis.
  arXiv  Detail & Related papers  (2026-02-12T06:32:49Z)
• Decoupling Amplitude and Phase Attention in Frequency Domain for RGB-Event based Visual Object Tracking [51.31378940976401]
  Existing RGB-Event tracking approaches fail to fully exploit the unique advantages of event cameras.<n>We propose a novel tracking framework that performs early fusion in the frequency domain, enabling effective aggregation of high-frequency information from the event modality.<n>Experiments on three widely used RGB-Event tracking benchmark datasets, including FE108, FELT, and COESOT, demonstrate the high performance and efficiency of our method.
  arXiv  Detail & Related papers  (2026-01-03T01:10:17Z)
• Differentially Private Feature Release for Wireless Sensing: Adaptive Privacy Budget Allocation on CSI Spectrograms [0.0]
  We study differentially private (DP) feature release for wireless sensing.<n>We propose an adaptive privacy budget allocation mechanism tailored to the highly non-uniform structure of CSI time-frequency representations.<n>Our method yields higher accuracy and lower error while substantially reducing empirical leakage in identity and membership inference attacks.
  arXiv  Detail & Related papers  (2025-12-23T12:45:49Z)
• It's Not the Target, It's the Background: Rethinking Infrared Small Target Detection via Deep Patch-Free Low-Rank Representations [5.326302374594885]
  In this paper, we propose a novel end-to-end IRSTD framework, termed LRRNet.<n>Inspired by the physical compressibility of cluttered scenes, our approach adopts a compression-reconstruction-subtraction paradigm.<n>Experiments on multiple public datasets demonstrate that LRRNet outperforms 38 state-of-the-art methods in terms of detection accuracy, robustness, and computational efficiency.
  arXiv  Detail & Related papers  (2025-06-12T07:24:45Z)
• DenoDet: Attention as Deformable Multi-Subspace Feature Denoising for Target Detection in SAR Images [20.11145540094807]
  We propose a network aided by explicit frequency domain transform to calibrate convolutional biases and pay more attention to high-frequencies. We design TransDeno, a dynamic frequency domain attention module that performs as a transform domain soft thresholding operation. Our plug-and-play TransDeno sets state-of-the-art scores on multiple SAR target detection datasets.
  arXiv  Detail & Related papers  (2024-06-05T01:05:26Z)
• Frequency Perception Network for Camouflaged Object Detection [51.26386921922031]
  We propose a novel learnable and separable frequency perception mechanism driven by the semantic hierarchy in the frequency domain.<n>Our entire network adopts a two-stage model, including a frequency-guided coarse localization stage and a detail-preserving fine localization stage.<n>Compared with the currently existing models, our proposed method achieves competitive performance in three popular benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-17T11:30:46Z)
• Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy [67.33715954653098]
  We propose a novel DPFL algorithm named DP-FedSAM, which leverages gradient perturbation to mitigate the negative impact of DP. Specifically, DP-FedSAM integrates Sharpness Aware of Minimization (SAM) to generate local flatness models with stability and weight robustness. To further reduce the magnitude random noise while achieving better performance, we propose DP-FedSAM-$top_k$ by adopting the local update sparsification technique.
  arXiv  Detail & Related papers  (2023-05-01T15:19:09Z)
• SRRT: Exploring Search Region Regulation for Visual Object Tracking [58.68120400180216]
  We propose a novel tracking paradigm, called Search Region Regulation Tracking (SRRT) SRRT applies a proposed search region regulator to estimate an optimal search region dynamically for each frame. On the large-scale LaSOT benchmark, SRRT improves SiamRPN++ and TransT with absolute gains of 4.6% and 3.1% in terms of AUC.
  arXiv  Detail & Related papers  (2022-07-10T11:18:26Z)
• A Differentially Private Framework for Deep Learning with Convexified Loss Functions [4.059849656394191]
  Differential privacy (DP) has been applied in deep learning for preserving privacy of the underlying training sets. Existing DP practice falls into three categories - objective perturbation, gradient perturbation and output perturbation. We propose a novel output perturbation framework by injecting DP noise into a randomly sampled neuron.
  arXiv  Detail & Related papers  (2022-04-03T11:10:05Z)
• Video-based Person Re-identification without Bells and Whistles [49.51670583977911]
  Video-based person re-identification (Re-ID) aims at matching the video tracklets with cropped video frames for identifying the pedestrians under different cameras. There exists severe spatial and temporal misalignment for those cropped tracklets due to the imperfect detection and tracking results generated with obsolete methods. We present a simple re-Detect and Link (DL) module which can effectively reduce those unexpected noise through applying the deep learning-based detection and tracking on the cropped tracklets.
  arXiv  Detail & Related papers  (2021-05-22T10:17:38Z)
• Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction [61.17654438176999]
  We propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design. Our proposed method obtains the frame-level AUROC score of 88.3% on the CUHK Avenue dataset.
  arXiv  Detail & Related papers  (2020-11-05T11:34:12Z)

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.