Formal verification method using Lipschitz optimization on homographies to certify vision network robustness to camera pose changes in predominantly planar scenes.
Formal Verification of CNN-based Perception Systems
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abstract
We address the problem of verifying neural-based perception systems implemented by convolutional neural networks. We define a notion of local robustness based on affine and photometric transformations. We show the notion cannot be captured by previously employed notions of robustness. The method proposed is based on reachability analysis for feed-forward neural networks and relies on MILP encodings of both the CNNs and transformations under question. We present an implementation and discuss the experimental results obtained for a CNN trained from the MNIST data set.
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cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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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.