arXiv:2608. 18900v2 Announce Type: replace Abstract: As AI systems are increasingly deployed in safety-critical application domains (e.
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TestifAI is a deep learning testing framework that estimates model robustness against combinations of semantic input perturbations such as blur, brightness, and zoom. It allows users to define operational conditions as structured spaces with discrete severity levels and query robustness for any combination. By employing partial model tomography, TestifAI reconstructs higher‑order perturbation effects from low‑order tests, achieving less than 7% estimation error while reducing inference counts by 60‑80% across five image and language classification tasks.
TestifAI is a deep learning testing framework that estimates model robustness against combinations of input perturbations efficiently. It allows users to define structured spaces of semantic perturbations and severity levels, then query robustness for any combination. By using partial model tomography, TestifAI reconstructs higher-order perturbation effects from low-order tests, achieving less than 7% error while reducing inferences by 60–80%.
TestifAI is a deep learning testing framework that estimates model robustness against combinations of input perturbations efficiently. It allows users to define structured spaces of semantic perturbations and severity levels, then query robustness for any combination. By employing partial model tomography, TestifAI reconstructs higher‑order perturbation effects from low‑order tests, achieving less than 7% error while cutting inference counts by 60‑80% on image and language classification tasks.
arXiv:2606. 16883v1 Announce Type: cross Abstract: Generalization is a critical property of data-driven models, particularly deep learning models deployed in safety-critical applications.
Deep learning models remain vulnerable to real-world input perturbations, especially when multiple corruptions co-occur in the same input (e. g.
arXiv:2606. 04310v1 Announce Type: new Abstract: Deep Neural Networks (DNNs) are increasingly being deployed in security-critical and safety-sensitive applications, which makes rigorous testing essential to identify and mitigate model weaknesses.