Neural network technology has achieved good results in many tasks, such as image classification. However, for some input examples of neural networks, after the addition of designed and imperceptible perturbations to the examples, these adversarial examples can change the output results of the original examples. For image classification problems, we derive low-dimensional attack perturbation solutions on multidimensional linear classifiers and extend them to multidimensional nonlinear neural networks. Based on this, a new adversarial example generation algorithm is designed to modify a specified number of pixels. The algorithm adopts a greedy iterative strategy, and gradually iteratively determines the importance and attack range of pixel points. Finally, experiments demonstrate that the algorithm-generated adversarial example is of good quality, and the effects of key parameters in the algorithm are also analyzed.

中文翻译：

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一种自适应神经网络K-Pixel对抗样本生成算法研究

神经网络技术在很多任务中都取得了很好的效果，比如图像分类。然而，对于神经网络的一些输入示例，在给示例添加设计的和不易察觉的扰动后，这些对抗性示例可以改变原始示例的输出结果。对于图像分类问题，我们在多维线性分类器上推导出低维攻击扰动解决方案，并将其扩展到多维非线性神经网络。基于此，设计了一种新的对抗样本生成算法来修改指定数量的像素。该算法采用贪心迭代策略，逐步迭代确定像素点的重要性和攻击范围。最后，实验证明算法生成的对抗样本质量很好，