研究人员开发了轻量级嵌入式卷积神经网络（ConvNet）集成模型，以提高阿拉伯手写字符识别的准确性，其准确性可与大型模型相媲美。另一项研究调查了这些模型的安全性，揭示了其在黑盒对抗性攻击面前存在显著漏洞。这些攻击对人类几乎难以察觉，在基准数据集上成功率高达100%，凸显了增强阿拉伯手写体识别（AHR）系统安全性的必要性。 AI
影响 强调了AI系统在模型效率和安全性之间的权衡，尤其是在手写体识别等专业任务中。
排序理由 两篇arXiv论文，详细介绍了关于阿拉伯手写体识别模型及其安全漏洞的新研究。
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Arabic Handwritten Character Recognition (AHCR) has recently advanced significantly with deep Convolutional Neural Networks (ConvNets). However, many models in the literature are deep and computationally expensive in terms of parameters and FLOPs, limiting their deployment on res…
Arabic handwriting recognition (AHR) has made significant progress with deep learning models. AHR research has largely focused on performance, with security receiving little attention. This study provides what appears to be a new line of inquiry by demonstrating the vulnerability…