In social networks, the Internet of Things, mobile computing, electronic commerce, and other fields, Quick Response (QR) codes have been widely used as the interface between online and offline scenarios. In offline lives, users can readily scan QR codes with smartphones to access online networks or remote devices. However, standard QR codes appear as random black-and-white modules that make users difficult to visually distinguish the encoding message in QR codes, which incurs users unwittingly to scan malicious QR codes distributed by hackers, and results in serious issues of cyber security and privacy leakage. In this paper, we propose a novel system that can embed safety identification information (SII) into QR codes, which conduces to avoid issues of cyber security and privacy leakage. Although some existing methods attempt to embed visual information into QR codes, these methods require complex pre-designed algorithms and leave some limitations in visual representation. Unlike them, our system employs the machine learning technique, which can naturally embed SII into QR codes without compromising the scanning-robustness and message preservation. The decoding time of our results is an average of 0.72 s which is similar to the standard QR code. Subjective and objective experiments show that our system can effectively produce embedded QR codes that are applicable in real-world life, and reach the state-of-the-art level.

中文翻译：

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SafeCoder：基于机器学习的编码系统，可将安全识别信息嵌入到二维码中


在社交网络、物联网、移动计算、电子商务等领域，二维码作为线上线下场景的接口被广泛应用。在线下生活中，用户可以轻松地用智能手机扫描二维码来访问在线网络或远程设备。然而，标准二维码呈现为随机的黑白模块，导致用户难以直观区分二维码中的编码信息，从而导致用户无意中扫描到黑客分发的恶意二维码，造成严重的网络安全和网络安全问题。隐私泄露。在本文中，我们提出了一种可以将安全识别信息（SII）嵌入到二维码中的新颖系统，这有助于避免网络安全和隐私泄露问题。尽管一些现有的方法试图将视觉信息嵌入到二维码中，但这些方法需要复杂的预先设计的算法，并且在视觉表示方面留下了一些限制。与它们不同的是，我们的系统采用机器学习技术，可以自然地将 SII 嵌入到 QR 码中，而不会影响扫描鲁棒性和消息保存。我们结果的解码时间平均为 0.72 秒，与标准 QR 码相似。主观和客观实验表明，我们的系统可以有效地生成适用于现实生活的嵌入式二维码，并达到最先进的水平。