arxiv: 2605.04675 · v1 · submitted 2026-05-06 · 💻 cs.CV

Recognition: 2 theorem links

· Lean Theorem

Physical Adversarial Clothing Evades Visible-Thermal Detectors via Non-Overlapping RGB-T Pattern

Xiaopei Zhu , Guanning Zeng , Zhanhao Hu , Jun Zhu , Xiaolin Hu

Authors on Pith no claims yet

Pith reviewed 2026-05-08 17:46 UTC · model grok-4.3

classification 💻 cs.CV

keywords physical adversarial attacksRGB-T object detectionnon-overlapping patternsadversarial clothingmultimodal fusionvisible-thermal detectorstransferable attacks

0 comments

The pith

Adversarial clothing with non-overlapping visible and thermal patterns evades RGB-T detectors in both digital and physical settings.

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

The paper shows that clothing can be printed with separate visible and thermal adversarial patterns that do not overlap on the fabric. This design avoids the light reduction that occurs when patterns overlap and is optimized on 3D models of a person to cover all viewing angles. The approach uses a spatial discrete-continuous optimization process to generate the patterns and achieves high success rates against multiple RGB-T detectors that fuse the two modalities differently. A fusion-stage ensemble further allows the same clothing to transfer effectively to detectors it was not trained on. Readers would care because RGB-T detection supports safety-critical systems such as autonomous driving under low light.

Core claim

Non-overlapping RGB-T patterns on adversarial clothing, generated via spatial discrete-continuous optimization on full-view 3D models, produce high attack success rates on visible-thermal detectors across different fusion architectures in both digital and physical worlds, while a fusion-stage ensemble improves transferability to unseen detectors.

What carries the argument

The non-overlapping RGB-T pattern (NORP) that places distinct visible and thermal adversarial materials on separate regions of the clothing, optimized by spatial discrete-continuous optimization (SDCO) on 3D human and clothing models to enable full 360-degree attacks.

Load-bearing premise

The 3D RGB-T models and material simulations accurately capture real-world lighting, thermal emission, and sensor responses across all viewing angles.

What would settle it

A controlled physical test in which the printed adversarial clothing is worn by a moving person under varied outdoor lighting and angles, then the attack success rate is measured against the simulated rates on the same detectors.

Figures

Figures reproduced from arXiv: 2605.04675 by Guanning Zeng, Jun Zhu, Xiaolin Hu, Xiaopei Zhu, Zhanhao Hu.

**Figure 1.** Figure 1: Demonstration of physical attacks against RGB-T de view at source ↗

**Figure 2.** Figure 2: The overall pipeline of the proposed method. We jointly optimizes visible and thermal patterns on 3D RGB-T clothing models view at source ↗

**Figure 3.** Figure 3: Illustration of the SDCO method. In SRD, black pixels view at source ↗

**Figure 4.** Figure 4: ASRs for different RGB-T detectors at various (a) dis view at source ↗

**Figure 5.** Figure 5: Visualization of physical RGB-T attacks across diverse scenes. Top row: indoor scenarios. Bottom row: outdoor scenarios. O: view at source ↗

read the original abstract

Visible-thermal (RGB-T) object detection is a crucial technology for applications such as autonomous driving, where multimodal fusion enhances performance in challenging conditions like low light. However, the security of RGB-T detectors, particularly in the physical world, has been largely overlooked. This paper proposes a novel approach to RGB-T physical attacks using adversarial clothing with a non-overlapping RGB-T pattern (NORP). To simulate full-view (0$^{\circ}$--360$^{\circ}$) RGB-T attacks, we construct 3D RGB-T models for human and adversarial clothing. NORP is a new adversarial pattern design using distinct visible and thermal materials without overlap, avoiding the light reduction in overlapping RGB-T patterns (ORP). To optimize the NORP on adversarial clothing, we propose a spatial discrete-continuous optimization (SDCO) method. We systematically evaluated our method on RGB-T detectors with different fusion architectures, demonstrating high attack success rates both in the digital and physical worlds. Additionally, we introduce a fusion-stage ensemble method that enhances the transferability of adversarial attacks across unseen RGB-T detectors with different fusion architectures.

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.

Desk Editor's Note private letter to a colleague

NORP plus SDCO gives a clean way to do physical attacks on RGB-T detectors without the usual modality interference, but the physical claims rest on unvalidated 3D and material simulations.

read the letter

This paper's main contribution is the non-overlapping RGB-T pattern (NORP) for adversarial clothing, combined with a spatial discrete-continuous optimization (SDCO) to make it work across all viewing angles on 3D human models. It also adds a fusion-stage ensemble to improve transfer to unseen detectors. What the work does well is identify a practical limitation in prior adversarial clothing attacks—overlapping patterns reduce effectiveness in one modality—and propose a fix using distinct materials. The 3D modeling for full 360-degree coverage is a solid step toward realistic physical evaluation, and testing across different fusion architectures shows some thought about real-world detector variations. The results claim strong performance in both digital and physical settings, which would be useful if the numbers hold. The potential issue is the sim-to-real transfer. The physical attacks rely on how accurately the 3D models and material properties simulate actual thermal emission, lighting, and sensor responses. If the paper doesn't include direct validation like side-by-side real and simulated images or error metrics on temperature, then the physical success rates might not generalize as claimed. That assumption is load-bearing for the main result. Overall, this is aimed at people studying physical adversarial attacks on multimodal systems. It has enough of a new idea and relevant application that it should go to peer review, though the authors will probably need to add more on the simulation fidelity to make the physical claims convincing.

Referee Report

3 major / 3 minor

Summary. The paper proposes non-overlapping RGB-T adversarial patterns (NORP) printed on clothing to evade visible-thermal (RGB-T) object detectors. It constructs 3D RGB-T models of humans and clothing to enable full 0°–360° view simulation, introduces a spatial discrete-continuous optimization (SDCO) procedure to generate the patterns, reports high attack success rates (ASR) against RGB-T detectors with varied fusion architectures in both digital and physical settings, and adds a fusion-stage ensemble to boost transferability to unseen detectors.

Significance. If the physical-world transfer results hold under rigorous validation, the work would be significant for highlighting practical vulnerabilities in multimodal RGB-T detectors used in safety-critical settings such as autonomous driving. The NORP design directly addresses light-reduction problems of overlapping patterns, the 3D full-view modeling is a reasonable attempt to handle viewpoint variation, and the ensemble technique targets a known weakness in adversarial transfer. These elements could inform future defense research if supported by stronger empirical grounding.

major comments (3)

[Physical evaluation] Physical-world evaluation section: high ASR is claimed for the fabricated NORP clothing across viewing angles and fusion architectures, yet no quantitative sim-to-real validation metrics (temperature prediction error, emissivity calibration error, or RGB-T image similarity scores between rendered and real captures) are supplied. Without these, it is impossible to determine whether the reported physical success stems from accurate modeling or from unmodeled factors, directly undermining the central transfer claim.
[Method] SDCO optimization and 3D model construction: the method optimizes patterns on simulated 3D RGB-T meshes, but the manuscript provides no ablation on the impact of material property assumptions (e.g., thermal emissivity values or non-overlapping layer interactions) or on how sensor response functions are approximated. These modeling choices are load-bearing for the assertion that NORP outperforms ORP and generalizes across architectures.
[Evaluation] Transferability experiments: the fusion-stage ensemble is presented as improving ASR on unseen detectors, but the evaluation lacks explicit baseline comparisons (e.g., single-model attacks or standard ensemble methods) and reports no statistical significance or variance across the tested fusion architectures, weakening the transferability conclusion.

minor comments (3)

[Figures] Figure captions for the physical clothing results should explicitly list the detector fusion types, viewing angles, and environmental conditions under which each image was captured.
[Method] The distinction between NORP and ORP would benefit from a short equation or pseudocode block defining the non-overlap constraint and the resulting radiance model.
[Related Work] A small number of recent references on thermal adversarial attacks and multimodal fusion defenses are missing from the related-work section.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We sincerely thank the referee for the constructive and detailed feedback. We address each major comment point by point below, providing clarifications and committing to revisions that strengthen the empirical grounding of our claims without altering the core contributions.

read point-by-point responses

Referee: [Physical evaluation] Physical-world evaluation section: high ASR is claimed for the fabricated NORP clothing across viewing angles and fusion architectures, yet no quantitative sim-to-real validation metrics (temperature prediction error, emissivity calibration error, or RGB-T image similarity scores between rendered and real captures) are supplied. Without these, it is impossible to determine whether the reported physical success stems from accurate modeling or from unmodeled factors, directly undermining the central transfer claim.

Authors: We thank the referee for this important point. Our physical evaluations used real captures of the printed NORP clothing under controlled indoor and outdoor conditions matching the simulation viewpoints, yielding the reported ASRs. However, we did not include explicit quantitative sim-to-real metrics in the original manuscript. In the revised version we will add: (i) temperature prediction errors computed by comparing simulated thermal maps against contactless thermometer measurements on the fabric surface, (ii) details of emissivity calibration using known reference materials, and (iii) RGB-T image similarity scores (SSIM and LPIPS) between rendered and captured pairs. These additions will directly address the concern and support the modeling fidelity. revision: yes
Referee: [Method] SDCO optimization and 3D model construction: the method optimizes patterns on simulated 3D RGB-T meshes, but the manuscript provides no ablation on the impact of material property assumptions (e.g., thermal emissivity values or non-overlapping layer interactions) or on how sensor response functions are approximated. These modeling choices are load-bearing for the assertion that NORP outperforms ORP and generalizes across architectures.

Authors: We appreciate the referee drawing attention to the modeling assumptions. The 3D RGB-T meshes use literature-standard emissivity values (0.85 for clothing, 0.95 for skin) and approximate sensor responses via typical RGB and LWIR spectral sensitivity curves; non-overlapping layers are modeled by independent material assignment without cross-layer thermal interaction. While these choices are justified by prior work, we agree an ablation would be valuable. The revised manuscript will include a new ablation subsection (and supplementary figures) varying emissivity by ±0.1, testing alternative sensor response approximations, and measuring resulting ASR changes for both NORP and ORP. This will confirm robustness and strengthen the generalization claims. revision: yes
Referee: [Evaluation] Transferability experiments: the fusion-stage ensemble is presented as improving ASR on unseen detectors, but the evaluation lacks explicit baseline comparisons (e.g., single-model attacks or standard ensemble methods) and reports no statistical significance or variance across the tested fusion architectures, weakening the transferability conclusion.

Authors: We agree that the transferability evaluation can be made more rigorous. The current results show the fusion-stage ensemble achieving higher ASR on held-out detectors than the individual models used for optimization. To address the gaps, the revision will add: explicit comparisons against single-model attacks and both input-stage and decision-stage ensemble baselines; mean ASR with standard deviation across five independent optimization runs; and statistical significance testing (paired t-tests with p-values) across the different fusion architectures. These updates will provide clearer evidence for the ensemble's benefit. revision: yes

Circularity Check

0 steps flagged

No circularity; derivation uses standard adversarial optimization and empirical evaluation on constructed models

full rationale

The paper's chain consists of constructing 3D RGB-T models, defining NORP as a non-overlapping pattern design, proposing SDCO for optimization, and reporting attack success rates on various fusion architectures in digital and physical settings. None of these steps reduce to self-definition, fitted inputs renamed as predictions, or load-bearing self-citations. The central claims rest on explicit simulation choices and experimental measurements rather than any quantity being equivalent to its inputs by construction. This is the expected non-finding for an empirical attack paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the work relies on standard 3D rendering and optimization techniques from prior adversarial ML literature.

pith-pipeline@v0.9.0 · 5507 in / 1021 out tokens · 30838 ms · 2026-05-08T17:46:56.091485+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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