AirflowAttack: Thermal-Airflow Adversarial Perturbations against Infrared Remote-Sensing Vision-Language Models

2026-07-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Remote Sensing AI · Depth: Expert, extended

Summary

AirflowAttack, the first adversarial attack for infrared (IR) remote-sensing Vision-Language Models (VLMs), introduces a novel perturbation method based on thermal-airflow turbulence. This lightweight generator synthesizes a single input-agnostic perturbation, achieving a mean zero-shot scene-classification attack success rate (ASR) of 48.5% across five diverse CLIP backbones, significantly surpassing four IR-specific physical baselines (27.7–37.0%). When applied to six state-of-the-art VLMs, AirflowAttack reduced scene-classification accuracy by up to 38.2% (relative). Intriguingly, some models exhibited increased confidence in their IR analysis, confabulating the perturbation as authentic thermal evidence. Ablation studies confirmed the airflow prior enhances physical plausibility without compromising attack efficacy, exposing critical vulnerabilities in the expanding IR VLM ecosystem.

Key takeaway

For AI Security Engineers and Machine Learning Engineers deploying infrared (IR) Vision-Language Models, AirflowAttack reveals a critical, modality-specific vulnerability. You must prioritize developing and implementing IR-specific defenses, such as robust detection or adversarial training, to mitigate physically plausible thermal-airflow perturbations. Be aware that your models might paradoxically increase confidence in false thermal cues, leading to dangerous misinterpretations in security-critical applications.

Key insights

Physically plausible thermal-airflow perturbations effectively exploit vulnerabilities in IR remote-sensing VLMs, even increasing model confidence in false thermal cues.

Principles

• Adversarial attacks can leverage physical phenomena.
• Domain-specific training may amplify VLM vulnerability.
• Universal perturbations transfer across architectures.

Method

AirflowAttack generates a universal adversarial perturbation using a lightweight generator, optimized with a confidence loss and an airflow-correlation loss to simulate thermal-airflow turbulence patterns.

In practice

• Evaluate IR VLM robustness against physical attacks.
• Develop IR-specific defenses for thermal perturbations.
• Audit VLM confidence for "adversarial confabulation."

Topics

• Infrared Remote Sensing
• Vision-Language Models
• Adversarial Attacks
• Thermal-Airflow Turbulence
• Model Robustness
• Scene Classification

Best for: Research Scientist, Computer Vision Engineer, AI Scientist, Machine Learning Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.