A tensor-based batch fuzzing framework with adaptive perturbation scaling from specification ranges achieves up to 40X higher throughput and 4X more detected violations than sequential baselines on DNN benchmarks.
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A ReLU-catalyzed abstraction method yields tighter bounds for transformer verification by converting dot-product constraints into ReLU forms that leverage standard convex relaxations.
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Tensor-Based Batch Fuzzing with Adaptive Perturbation Scaling for Deep Neural Networks
A tensor-based batch fuzzing framework with adaptive perturbation scaling from specification ranges achieves up to 40X higher throughput and 4X more detected violations than sequential baselines on DNN benchmarks.