ALLUDE: A Unified Evaluation System for Configurable Attacks in Differentiable Environments
Adversarial attacks against vision models like object detectors are often evaluated under limited conditions, leaving their performance under-characterized. Bridging simulation and differentiable rendering enables more robust, end-to-end evaluation of these adversarial attacks, yet there is no easy-to-use, unified system that offers a rich set of customizable configurations for adversarial attacks across multiple scenes, objects, environmental and lighting conditions, and camera trajectories. We present ALLUDE, which addresses these gaps, offering first-of-its-kind evaluation capabilities acro
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- PossiblePossibly related (embedding) · 50%Into the Omniverse: Three Workflows for Improving Vision AI Agent Accuracy With Synthetic Data and Fine-Tuning →
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%browser-use/browser-use →
“Shared author/contributor keys: lee”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Mansi Phute →
“ALLUDE: A Unified Evaluation System for Configurable Attacks in Differentiable Environments”
- LinkedLinked via arxiv author · 85%Alexander Greenhalgh →
“ALLUDE: A Unified Evaluation System for Configurable Attacks in Differentiable Environments”
- LinkedLinked via arxiv author · 85%Matthew Hull →
“ALLUDE: A Unified Evaluation System for Configurable Attacks in Differentiable Environments”
- LinkedLinked via arxiv author · 85%Haoran Wang →
“ALLUDE: A Unified Evaluation System for Configurable Attacks in Differentiable Environments”