The widespread adoption of QR codes has revolutionized various industries, streamlined transactions and improved inventory management. However, this increased reliance on QR code technology also exposes it to potentia...The widespread adoption of QR codes has revolutionized various industries, streamlined transactions and improved inventory management. However, this increased reliance on QR code technology also exposes it to potential security risks that malicious actors can exploit. QR code Phishing, or “Quishing”, is a type of phishing attack that leverages QR codes to deceive individuals into visiting malicious websites or downloading harmful software. These attacks can be particularly effective due to the growing popularity and trust in QR codes. This paper examines the importance of enhancing the security of QR codes through the utilization of artificial intelligence (AI). The abstract investigates the integration of AI methods for identifying and mitigating security threats associated with QR code usage. By assessing the current state of QR code security and evaluating the effectiveness of AI-driven solutions, this research aims to propose comprehensive strategies for strengthening QR code technology’s resilience. The study contributes to discussions on secure data encoding and retrieval, providing valuable insights into the evolving synergy between QR codes and AI for the advancement of secure digital communication.展开更多
针对MAP(maximum a posteriori)框架下QR码(quick response code)去模糊方法复原效果不佳、运行时间长的问题,为提高模糊QR码的解码能力,提出一种QR码快速去模糊方法。在改进的MAP框架下引入基于灰度分布特性的图像先验,约束复原图像的...针对MAP(maximum a posteriori)框架下QR码(quick response code)去模糊方法复原效果不佳、运行时间长的问题,为提高模糊QR码的解码能力,提出一种QR码快速去模糊方法。在改进的MAP框架下引入基于灰度分布特性的图像先验,约束复原图像的二值特性,可有效提高QR码的复原效果,并采用改进的多尺度模糊核估计方法,抑制噪声干扰。实验表明,相比于其他基于MAP框架的二值图像去模糊方法,该方法在复原效果、运行时间以及复原后图像的识别率上均有明显优势。展开更多
文摘The widespread adoption of QR codes has revolutionized various industries, streamlined transactions and improved inventory management. However, this increased reliance on QR code technology also exposes it to potential security risks that malicious actors can exploit. QR code Phishing, or “Quishing”, is a type of phishing attack that leverages QR codes to deceive individuals into visiting malicious websites or downloading harmful software. These attacks can be particularly effective due to the growing popularity and trust in QR codes. This paper examines the importance of enhancing the security of QR codes through the utilization of artificial intelligence (AI). The abstract investigates the integration of AI methods for identifying and mitigating security threats associated with QR code usage. By assessing the current state of QR code security and evaluating the effectiveness of AI-driven solutions, this research aims to propose comprehensive strategies for strengthening QR code technology’s resilience. The study contributes to discussions on secure data encoding and retrieval, providing valuable insights into the evolving synergy between QR codes and AI for the advancement of secure digital communication.
文摘针对MAP(maximum a posteriori)框架下QR码(quick response code)去模糊方法复原效果不佳、运行时间长的问题,为提高模糊QR码的解码能力,提出一种QR码快速去模糊方法。在改进的MAP框架下引入基于灰度分布特性的图像先验,约束复原图像的二值特性,可有效提高QR码的复原效果,并采用改进的多尺度模糊核估计方法,抑制噪声干扰。实验表明,相比于其他基于MAP框架的二值图像去模糊方法,该方法在复原效果、运行时间以及复原后图像的识别率上均有明显优势。
