Grey-Box Adversarial Patch for Velocity Deception in Monocular ORB-SLAM3

We propose a grey-box physical-style adversarial patch that deceives the velocity estimation of classical monocular ORB-SLAM3 without modifying its source code. Instead of relying on differentiable surrogate models or loop-closure corruption, we explicitly target the ORB feature matching layer and bias it towards a larger fraction of almost-stationary but high-confidence correspondences between consecutive frames. To this end, we define a fitness score that rewards ORB matches with low Hamming distance and small pixel displacement under a simulated forward motion (zoom-in), and optimize the patch using a lightweight evolutionary algorithm (EA). On the EuRoC MAV V1_01 sequence with unmodified C++ ORB-SLAM3, our final patch increases ATE (RMSE) from 0.0876 m to 1.7717 m (about 20x), shortens the reconstructed trajectory length by 29%, and induces long intervals of near-zero estimated velocity with spiky artifacts. We discuss system-level implications for feedback control and simple defenses based on match-motion consistency.