Formal verification method using Lipschitz optimization on homographies to certify vision network robustness to camera pose changes in predominantly planar scenes.
Available: https://arxiv.org/abs/2405.01349
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Auto-ART delivers the first structured synthesis of adversarial robustness consensus plus an executable multi-norm testing framework that flags gradient masking in 92% of cases on RobustBench and reveals a 23.5 pp robustness gap.
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Lipschitz Optimization for Formal Verification of Homographies
Formal verification method using Lipschitz optimization on homographies to certify vision network robustness to camera pose changes in predominantly planar scenes.
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Auto-ART: Structured Literature Synthesis and Automated Adversarial Robustness Testing
Auto-ART delivers the first structured synthesis of adversarial robustness consensus plus an executable multi-norm testing framework that flags gradient masking in 92% of cases on RobustBench and reveals a 23.5 pp robustness gap.