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arXiv Machine Learning By Dietmar Saupe

Adversarial Stress Testing of Outlier Detection in Subjective Image Quality Assessment

Read the original on arXiv Machine Learning →

The paper introduces a general empirical worst‑case framework for testing outlier‑detection methods in subjective image quality assessment. It presents adversarial attack generators that optimize ratings to maximize the discrepancy between mean opinion scores (MOS) and ground truth, applying these to both discrete and continuous rating scales. The study evaluates several hard and soft outlier‑detection techniques, revealing significant differences in worst‑case performance and proposes low‑complexity methods that achieve strong empirical results.

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