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arxiv: 2410.18717 · v2 · submitted 2024-10-24 · 💻 cs.CV · cs.AI

Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy Versus Performance

Mulugeta Weldezgina Asres , Lei Jiao , Christian Walter Omlin This is my paper

Pith reviewed 2026-05-23 18:22 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords video anonymizationanomaly detectionprivacy protectionreal-time processingLA3Dsurveillance video
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The pith

The LA3D method substantially improves privacy in video anomaly detection without severely degrading detection performance.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes LA3D, a lightweight adaptive anonymization technique for real-time video anomaly detection. It uses dynamic adjustment to strengthen full-body privacy protection in surveillance videos. Evaluations on public datasets show that LA3D raises privacy levels while keeping most of the utility for anomaly detection intact. The method outperforms both conventional anonymization techniques and deep learning approaches on the privacy-performance trade-off.

Core claim

LA3D enables substantial improvement in privacy anonymization without severely degrading VAD efficacy, outperforming conventional and deep learning approaches.

What carries the argument

LA3D, a lightweight adaptive anonymization for VAD that employs dynamic adjustment to enhance full-body privacy protection.

Load-bearing premise

The dynamic adjustment mechanism in LA3D achieves full-body privacy protection and that the evaluations on public datasets generalize to real-world VAD applications.

What would settle it

A real-world test showing that LA3D leaves identifiable body features visible or causes a large drop in anomaly detection accuracy compared to non-anonymized video.

Figures

Figures reproduced from arXiv: 2410.18717 by Christian Walter Omlin, Lei Jiao, Mulugeta Weldezgina Asres.

Figure 1
Figure 1. Figure 1: Conventional AN techniques on sample images from the VISPR dataset: (left to right) RAW_IMAGE, [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The privacy attribute class distribution of the VISPR train set. The estimated relative label distribution [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Visual comparison of different AN methods on sample images from the VISPR dataset: (left to [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Impact of the αr on images with different resolutions from the VISPR dataset. (Top to bottom) image resolution Z: [160 × 120], [320 × 240], and [1280 × 960]. (Left to right) 1:RAW_IMAGE, 2:PIXELIZED_D4_A (αb = 0.5, αr = 0.5), 3:PIXELIZED_D4_A (αb = 0.5, αr = Z/Zref), 4:PIXELIZED_A (ismax = T rue, Da = Zb), 5:BLURRED_A (αb = 0.5, αr = 0.5), 6:BLURRED_A (αb = 0.5, αr = Z/Zref), 7:BLURRED_A (αb = 0.5, αr = Z/… view at source ↗
Figure 5
Figure 5. Figure 5: Images consisting of persons at different depths on sample images from the VISPR dataset: (left to right) [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: PD per each privacy attribute on the VISPR dataset. [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Sample images with a person holding an object, ReID leakage after AN due to the induced match through [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: VAD versus PD performance of the AN methods with a) the PEL4VAD, and b) the MGFN models. [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
read the original abstract

Recent advancements in artificial intelligence hold ample potential for monitoring applications using surveillance cameras. However, concerns about privacy and model bias have made it challenging to utilize them in public. Although de-identification approaches have been proposed in the literature, aiming to achieve a certain level of anonymization (AN), most of them employ deep learning models that are computationally demanding for real-time edge deployment. This study revisits conventional AN solutions for privacy protection and real-time video anomaly detection (VAD) applications. We propose a lightweight adaptive AN for VAD (LA3D) that employs dynamic adjustment to enhance full-body privacy protection. We have evaluated privacy protection and VAD utility retention efficacy using several publicly available datasets to examine the strengths and weaknesses of different AN methods and highlight the promising leverage of our approach. Our experiment demonstrates that the LA3D enables substantial improvement in privacy AN without severely degrading VAD efficacy, outperforming conventional and deep learning approaches. Code is available at https://github.com/muleina/LA3D .

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper proposes LA3D, a lightweight adaptive anonymization method for video anomaly detection (VAD) that uses dynamic adjustment to enhance full-body privacy protection. It claims evaluations on several public datasets show substantial privacy AN gains without severely degrading VAD efficacy and that LA3D outperforms conventional and deep learning approaches. Code is made available via GitHub.

Significance. If the claimed results hold, this would be significant for practical edge deployment of privacy-preserving VAD systems, as it targets the computational barriers of deep learning anonymization while addressing privacy-utility tradeoffs in real-time surveillance, a key enabler for public monitoring applications.

major comments (1)
  1. [Abstract] Abstract: The central claim that 'Our experiment demonstrates that the LA3D enables substantial improvement in privacy AN without severely degrading VAD efficacy, outperforming conventional and deep learning approaches' is presented with no supporting evidence. No privacy metric, VAD metric (e.g., AUC), datasets, baselines, numerical deltas, statistical tests, or description of the dynamic adjustment algorithm are supplied, rendering the tradeoff and superiority assertions unverifiable and unsupported.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for highlighting the need for greater specificity in the abstract. We address the concern directly below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that 'Our experiment demonstrates that the LA3D enables substantial improvement in privacy AN without severely degrading VAD efficacy, outperforming conventional and deep learning approaches' is presented with no supporting evidence. No privacy metric, VAD metric (e.g., AUC), datasets, baselines, numerical deltas, statistical tests, or description of the dynamic adjustment algorithm are supplied, rendering the tradeoff and superiority assertions unverifiable and unsupported.

    Authors: We agree that the abstract as written is too high-level and does not include the requested quantitative details or method description, which limits verifiability from the abstract alone. The full manuscript contains the missing elements: privacy metrics (AN scores), VAD performance (AUC), specific public datasets, baseline comparisons (conventional and deep-learning methods), numerical deltas, and the dynamic adjustment algorithm. To strengthen the abstract, we will revise it to incorporate key results and a brief method outline while respecting length constraints. revision: yes

Circularity Check

0 steps flagged

No circularity; experimental claims rest on external dataset evaluations rather than self-referential definitions or fits.

full rationale

The paper's central claim is an empirical assertion that LA3D improves privacy AN while retaining VAD efficacy and outperforming baselines, evaluated on public datasets. No equations, parameters fitted to target metrics, self-citations, uniqueness theorems, or ansatzes appear in the provided abstract. The derivation chain is therefore absent; the result is presented as the outcome of independent experiments rather than a quantity forced by construction from its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract does not specify any free parameters, axioms, or invented entities underlying the proposed method.

pith-pipeline@v0.9.0 · 5687 in / 905 out tokens · 57122 ms · 2026-05-23T18:22:45.012935+00:00 · methodology

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Reference graph

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