Beyond the Frame: Redundancy, Not Trajectory, Carries the Privacy Leak in Video Unlearning
Class unlearning for video segmentation aims to ensure that a removed category cannot be reconstructed from a deployed model's outputs. Current leakage evaluation algorithms inspect one frame at a time, and we show that this understates leakage by a wide margin: an attacker that averages a model's output field over a two-second clip recovers the forgotten class from every method we evaluate on VSPW and most on Virtual KITTI~2, including models whose per-frame predictions are visually indistinguishable from a retrain oracle (a model trained from scratch without the forgotten class). We attribute the recoverable signal to cross-frame redundancy rather than object trajectory, establish this ...
| Authors | Kavyansh Tyagi, Paolo Russo |
|---|---|
| Primary category | cs.CV |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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