一句话：提出 GENFUZZER，一种生成式 AI 驱动的覆盖率引导数据增强模糊测试技术，为深度学习模型生成语义丰富、信息量高的测试数据集。
最先进的深度学习模型性能高度依赖于能够充分覆盖运行域的高质量训练与测试数据集，而数据采集与标注耗时且昂贵，数据增强因而成为缓解数据稀缺的有效手段。然而现有数据增强技术主要局限于噪声、翻转、缩放等简单几何与颜色空间变换，生成的数据集多样性有限，用于测试深度学习模型时所得结果往往无法反映模型的鲁棒性。为填补这一空白，作者提出 GENFUZZER：一种由生成式 AI 支撑、以覆盖率引导的深度学习模型数据增强模糊测试技术。该工作在图像分类任务中广泛采用的数据集与模型上进行验证，表明其能够生成更具信息量的数据集，使常用覆盖率准则最高提升 26%。
The performance of state-of-the-art Deep Learning models heavily depends on the availability of well-curated training and testing datasets that sufficiently capture the operational domain. Data augmentation is an effective technique in alleviating data scarcity, reducing the time-consuming and expensive data collection and labelling processes. Despite their potential, existing data augmentation techniques primarily focus on simple geometric and colour space transformations, like noise, flipping and resizing, producing datasets with limited diversity. When the augmented dataset is used for testing the Deep Learning models, the derived results are typically uninformative about the robustness of the models. We address this gap by introducing GENFUZZER, a novel coverage-guided data augmentation fuzzing technique for Deep Learning models underpinned by generative AI. We demonstrate our approach using widely-adopted datasets and models employed for image classification, illustrating its effectiveness in generating informative datasets leading up to a 26% increase in widely-used coverage criteria.