A single input-agnostic thermal-airflow perturbation, optimized on one surrogate CLIP model, transfers to five CLIP backbones and six VLMs, degrading scene classification by up to 38.2%.
In: International conference on machine learning
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
A^4D detects adversarial attacks in an attack- and classifier-agnostic way by measuring non-arbitrary shifts in CLIP embedding space from prompt-based similarity scores.
A CNN-plus-quantum-circuit classifier with learned fusion reports lower attack success rates and much higher attack-generation cost than a CNN baseline on MNIST, OrganAMNIST, and CIFAR-10.
citing papers explorer
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AirflowAttack: Thermal-Airflow Adversarial Perturbations against Infrared Remote-Sensing Vision-Language Models
A single input-agnostic thermal-airflow perturbation, optimized on one surrogate CLIP model, transfers to five CLIP backbones and six VLMs, degrading scene classification by up to 38.2%.
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A Classifier-Agnostic Zero-Shot Adversarial Attack Detection via CLIP
A^4D detects adversarial attacks in an attack- and classifier-agnostic way by measuring non-arbitrary shifts in CLIP embedding space from prompt-based similarity scores.
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QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits
A CNN-plus-quantum-circuit classifier with learned fusion reports lower attack success rates and much higher attack-generation cost than a CNN baseline on MNIST, OrganAMNIST, and CIFAR-10.