通过潜空间截断提升生成测试用例的质量和故障检测率。
Latent Regularization in Generative Test Input Generation
- 用潜空间混合与二分搜索优化截断，生成更优测试样本。
- 在三个数据集上，故障检测率、多样性与有效性均优于随机截断。
- 适合关注模型鲁棒性测试的研究者和工程师使用。
本研究探讨了通过截断对潜空间进行正则化对深度学习分类器生成测试输入质量的影响。采用基于风格的GAN这一先进生成方法，在MNIST、Fashion MNIST和CIFAR-10三个数据集上评估其在边界测试中的表现。从有效性、多样性和故障检测三个维度进行分析，比较了潜空间混合结合二分搜索优化与随机潜空间截断两种策略。实验表明，潜空间混合策略在提升故障检测率的同时，也显著改善了样本的有效性和多样性。
原文摘要 · Abstract (English)
This study investigates the impact of regularization of latent spaces through truncation on the quality of generated test inputs for deep learning classifiers. We evaluate this effect using style-based GANs, a state-of-the-art generative approach, and assess quality along three dimensions: validity, diversity, and fault detection. We evaluate our approach on the boundary testing of deep learning image classifiers across three datasets, MNIST, Fashion MNIST, and CIFAR-10. We compare two truncation strategies: latent code mixing with binary search optimization and random latent truncation for generative exploration. Our experiments show that the latent code-mixing approach yields a higher fault detection rate than random truncation, while also improving both diversity and validity.
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