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A Survey of Black-Box Adversarial Attacks on Computer Vision Models
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A Survey of Black-Box Adversarial Attacks on Computer Vision Models
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Machine learning has seen tremendous advances in the past few years, which has lead to deep learning models being deployed in varied applications of day-to-day life. Attacks on such models using perturbations, particularly in real-life scenarios, pose a severe challenge to their applicability, pushing research into the direction which aims to enhance the robustness of these models. After the introduction of these perturbations by Szegedy et al. [1], significant amount of research has focused on the reliability of such models, primarily in two aspects - white-box, where the adversary has access to the targeted model and related parameters; and the black-box, which resembles a real-life scenario with the adversary having almost no knowledge of the model to be attacked. To provide a comprehensive security cover, it is essential to identify, study, and build defenses against such attacks. Hence, in this paper, we propose to present a comprehensive comparative study of various black-box adversarial attacks and defense techniques.
Forward citations
Cited by 4 Pith papers
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A Unified Perspective on Adversarial Membership Manipulation in Vision Models
Adversarial perturbations reliably fabricate membership signals in vision-model MIAs, separated by a gradient-norm collapse trajectory that enables robust detection and inference.
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AdvNav: Behavior-Guided Black-Box Adversarial Attacks on Vision-Language Navigation
AdvNav disrupts multi-step vision-language navigation with gradient-free, behavior-guided visual noise, reaching 49.7–87.3% attack success on HAMT and MapGPT without model internals.
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AdvNav: Behavior-Guided Black-Box Adversarial Attacks on Vision-Language Navigation
AdvNav is a gradient-free attack that overlays Perlin noise on a VLN agent's camera and uses behavior feedback plus genetic search, breaking 49.70-87.30% of successful R2R navigations.
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False Alarms, Real Damage: Adversarial Attacks Using LLM-based Models on Text-based Cyber Threat Intelligence Systems
LLM-generated adversarial fake text can perform evasion, flooding, and poisoning attacks that mislead and degrade text-based CTI pipelines.
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