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 across Linux and Windows. We comprehensively demonstrate ALLUDE's evaluation breadth through a two-pr...
| Authors | Mansi Phute, Alexander Greenhalgh, Matthew Hull et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 July 2026 |
| Last updated at source | 19 July 2026 |
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