With the research advancement of Artificial Intelligence in the last years, there are new opportunities to mitigate real-world problems and advance technologically. Image recognition models in particular, are assigned with perception tasks to mitigate complex real-world challenges and lead to new solutions. Furthermore, the computational complexity and demand for resources of such models has also increased. To mitigate this, model optimization and hardware acceleration has come into play, but effectively integrating such concepts is a challenging and error-prone process. In order to allow developers and researchers to explore the robustness of deep learning image recognition models deployed on different hardware acceleration devices, we propose MutateNN, a tool that provides mutation testing and analysis capabilities for that purpose. To showcase its capabilities, we utilized 21 mutations for 7 widely-known pre-trained deep neural network models. We deployed our mutants on 4 different devices of varying computational capabilities and observed discrepancies in mutants related to conditional operations, as well as some unstable behaviour with those related to arithmetic types.

翻译：随着近年来人工智能的研究进展，解决现实世界问题并实现技术革新的新机遇不断涌现。特别是图像识别模型，被赋予了感知任务以应对复杂的现实挑战并催生新解决方案。此外，这类模型的计算复杂度与资源需求也持续增长。为缓解这一问题，模型优化与硬件加速技术应运而生，但有效整合这些概念仍是一个充满挑战且易出错的过程。为使开发者和研究人员能够探索部署在不同硬件加速设备上的深度学习图像识别模型的鲁棒性，我们提出MutateNN——一个为此提供变异测试与分析能力的工具。为展示其能力，我们对7个广泛使用的预训练深度神经网络模型应用了21种变异。我们将这些变异体部署在4种计算能力各异的设备上，并观察到与条件运算相关的变异体存在差异，以及与算术类型相关的变异体出现不稳定行为。