GeoThreat: Transferable Targeted Adversarial Attacks on Large Vision-Language Models for Remote Sensing Image Interpretation

2026-07-23 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition, Cybersecurity & Data Privacy · Depth: Expert, quick

Summary

GeoThreat is a novel transferable targeted adversarial attack method designed for Large Vision-Language Models (LVLMs) used in remote sensing image interpretation. Unlike existing attacks that focus on general vision-language tasks, GeoThreat addresses the unique challenges of remote sensing, which demands joint reasoning over local discriminative cues and global scene context, particularly in black-box settings. The method modulates adversarial representations at both conceptual and perceptual levels. It employs class tokens from surrogate image encoders for conceptual representation and distills perceptual representations from patch tokens using collaborative importance estimation. GeoThreat incorporates adversarial-target similarity gradients to accurately characterize the relevance of local visual cues to the intended semantic manipulation. Perceptual representations are dynamically aligned with target patch tokens in a cross-attentive manner, adapting local cues to designated semantic details. Adversarial perturbations are iteratively updated through ensemble-based joint optimization of conceptual calibration and perceptual adaptation, demonstrating superior transferability and controllability across diverse LVLMs.

Key takeaway

For AI Security Engineers evaluating the robustness of Large Vision-Language Models in remote sensing applications, GeoThreat highlights a significant vulnerability. You should prioritize developing defenses that specifically counter attacks modulating both conceptual and perceptual representations. Your current robustness testing protocols may be insufficient against such transferable targeted attacks, necessitating an update to include more sophisticated adversarial example generation techniques.

Key insights

GeoThreat enables transferable targeted adversarial attacks on remote sensing LVLMs by modulating conceptual and perceptual representations for semantic manipulation.

Principles

• Remote sensing LVLMs demand joint local/global context reasoning.
• Adversarial attacks assess LVLM cross-modal semantic robustness.
• Targeted semantic manipulation requires conceptual and perceptual modulation.

Method

GeoThreat iteratively updates adversarial perturbations via ensemble-based joint optimization. It uses class tokens for conceptual calibration and dynamically aligns importance-estimated patch tokens with target patch tokens for perceptual adaptation.

In practice

• Assess LVLM robustness in remote sensing.
• Develop more robust remote sensing LVLMs.
• Generate targeted adversarial examples for LVLMs.

Topics

• Adversarial Attacks
• Large Vision-Language Models
• Remote Sensing
• Image Interpretation
• Model Robustness
• Black-box Attacks

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.