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New AI models for Arabic handwriting recognition vulnerable to attacks

作者 PulseAugur 编辑部 · [2 个来源] ·

Researchers have developed lightweight embedded ConvNet ensembles to improve Arabic handwritten character recognition, achieving accuracy comparable to larger models. A separate study investigated the security of these models, revealing significant vulnerabilities to black-box adversarial attacks. These attacks, which are nearly imperceptible to humans, achieved success rates of up to 100% on benchmark datasets, highlighting the need for enhanced security in AHR systems. AI

影响 Highlights the trade-off between model efficiency and security in AI systems, particularly for specialized tasks like handwriting recognition.

排序理由 Two arXiv papers detailing new research on Arabic handwriting recognition models and their security vulnerabilities.

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

New AI models for Arabic handwriting recognition vulnerable to attacks

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Abdelillah Semma ·

    嵌入式卷积神经网络集成：一种识别阿拉伯手写字符的轻量级方法

    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…

  2. arXiv cs.CV TIER_1 English(EN) · Rachid Elouahbi ·

    阿拉伯手写识别面临的威胁：对嵌入式ConvNet模型的黑盒对抗性攻击研究

    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…

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