EventCert: Certified Robustness of Event-Based Classifiers to Structured Deletion

Event cameras record the changes in a scene as a stream of events, which is turned into a tensor so that standard models can process it efficiently. In practice, packet loss, sensor faults, or occlusion can remove whole blocks of this tensor at once. In a traditional camera, such a loss does limited damage because neighbouring pixels carry similar information. Such redundancy is almost absent in event streams. As a result, a lost block is lost for good and can change a classifier’s prediction, posing serious risks in safety-critical applications such as robotics and driving. It is therefore essential to prove how many blocks can be removed while the prediction is guaranteed to stay the same, a question existing certification methods have largely left unexplored. We close this gap with EventCert, a method that proves, with high probability, how many blocks each prediction can lose without changing. It mirrors how events are deleted and certifies how many blocks an adversary may remove. Because only blocks that contain events can change the prediction, it turns this worst-case certificate into the first exact guarantees for random and bursty channel loss, and for an adversary and a lossy channel acting together, all from a single certification run. On five event-camera and neuromorphic-audio benchmarks, we compare EventCert with three existing certification methods: Gaussian smoothing, randomized ablation, and random deletion of single events. EventCert certifies 3.8–5.6× more deleted blocks than the best of them, while the other two certify almost none. On N-MNIST, 89% of test inputs stay certified even when an adversary removes any 8 blocks. While standard metrics only tell us how often a model is right, EventCert tells us, for each prediction, whether it will stay the same when part of the data is lost. By telling when an event-camera decision can still be trusted and when it cannot, EventCert can make self-driving cars safer, drones more reliable, and autonomous robots more effective.