主观图像质量评估中异常值检测的对抗性压力测试
Adversarial Stress Testing of Outlier Detection in Subjective Image Quality Assessment
AI总结：
针对主观图像质量评估中异常值检测方法的性能评估缺口，提出通用经验最坏情况框架，验证其能揭示方法的最坏情况表现，还提出低复杂度异常值检测方法，实现出色的最坏情况性能。
摘要：
在主观图像与视频质量评估中，观察者会对选定的刺激进行评分或比较。在计算平均意见得分（MOS）之前，不可靠的评分应被识别并作为异常值处理。现有多种异常值检测方法，包括标准化流程，但它们的对比性能常仅通过随机点击器等特定类型的合成异常值评估，这类测试未必能揭示方法的最坏情况表现。为解决该缺口，我们提出并验证了一种针对异常值检测方法的通用经验最坏情况框架，包含针对离散绝对类别评分与连续视觉模拟量表评分的概念验证对抗攻击生成器。这些攻击采用优化算法识别评分，使生成的MOS估计值与真实值的差异最大化。我们将该框架应用于多种硬、软异常值检测方法，证明它们在对抗性压力下的最坏情况重建性能存在显著差异，还提出了几种低复杂度的异常值检测方法，具备出色的经验最坏情况性能。
英文摘要：
In subjective image and video quality assessment, observers rate or compare selected stimuli. Before calculating mean opinion scores (MOSs), unreliable ratings should be identified and handled as outliers. Several outlier-detection methods are available, including standardized procedures, but their comparative performance is often evaluated using only specific types of synthetic outliers such as random clickers. Such tests do not necessarily reveal the worst-case behavior of these methods. To address this gap, we introduce and demonstrate a general empirical worst-case framework for outlier-detection methods, with proof-of-concept adversarial attack generators for both discrete absolute category and continuous visual analog scale ratings. The attacks use optimization algorithms to identify ratings that maximize the discrepancy between the resulting MOS estimates and the ground truth. We apply the proposed framework to several hard and soft outlier-detection methods and demonstrate substantial differences in their worst-case reconstruction performance under adversarial stress. We also propose several low-complexity outlier-detection methods that achieve excellent empirical worst-case performance.
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