Fast SDP certification of neural networks : towards large multi-class datasets

• URL: http://arxiv.org/abs/2607.03232v1
• Date: Fri, 03 Jul 2026 11:41:42 GMT
• Title: Fast SDP certification of neural networks : towards large multi-class datasets
• Abstract summary: We present a new quadratic model for the certification problem in adversarial robustness, which simultaneously accounts for all possible target classes.<n>A key advantage of our approach is that it certifies robustness in a single optimization, avoiding the need for a separate resolution per class.
• Score: 3.383208046995159
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a new quadratic model for the certification problem in adversarial robustness, which simultaneously accounts for all possible target classes. Building on this model, we propose a novel semidefinite programming (SDP) relaxation for incomplete verification. A key advantage of our approach is that it certifies robustness in a single optimization, avoiding the need for a separate resolution per class. This yields a significant computational speed-up and enables scalability to large datasets with many classes. To further improve efficiency, we also propose an effective pruning strategy of active neurons, thus reducing the problem dimensionality and accelerating convergence.

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