Safe4D: Causal Video Protection against Unauthorized 4D Reconstruction

Recent feed-forward geometry models recover cameras, depth and dynamic point clouds from ordinary video, either offline from a complete clip or online as frames arrive, turning a video into a renderable 4D asset within minutes. However, this capability raises serious concerns about privacy and intellectual property, since anyone who records a published video can recover the shape and motion of the people, products and places it shows. Existing protection methods perturb images or complete videos before release, but live broadcasts and video calls are released causally: each frame is published as soon as it is captured and cannot be revised, while viewers can record the stream and reconstruct it. Protecting video against 4D reconstruction under such causal release remains unexplored and raises three challenges: (i) temporal detachment: a perturbation fixed in image coordinates drifts off the moving content it protects and is averaged out by motion-aligned purification; (ii) purification uncertainty: the attacker may reconstruct from the raw or the purified video, and a perturbation tuned to one input leaves the other unprotected; and (iii) irrevocable release: each frame is transmitted as an 8-bit image as soon as it is protected, so an update weakened by rounding cannot be corrected later. To address these challenges, we propose Safe4D, the first video protection method against 4D reconstruction under causal release. Safe4D transports the perturbation committed at the previous frame along the estimated scene motion, so that the protection stays attached to the scene points it perturbs. Moreover, we introduce a purification-aware objective on a frozen streaming surrogate, which maximizes the weaker of the depth discrepancies on the direct and the temporally averaged stream, and a verified commit that accepts a sparse 8-bit update only if the quantized frame to be released improves this objective. Empirically, quantitative and qualitative results on four dynamic benchmarks show that Safe4D effectively disrupts the geometry recovered by streaming 4D reconstruction and remains robust under both causal and non-causal temporal purification.