ICStega：基于图像描述的语义可控语言隐写
ICStega: Image Captioning-based Semantically Controllable Linguistic Steganography
摘要：
如今，社交媒体已成为网络用户首选的交流平台，但也带来了安全威胁。语言隐写术将秘密信息隐藏在文本中，并发送给预定的接收者，实现隐蔽通信。与基于编辑的语言隐写相比，基于生成的方法大大提高了有效载荷容量。然而，现有的方法只能生成单独的隐写文本。社交媒体中的另一个常见行为是发送语义相关的图像-文本对。本文提出了一种基于图像描述的隐写系统，将秘密信息嵌入到生成的字幕中。因此，可以控制隐写文本的语义，并且可以通过发送语义相关的图像-文本对来传输秘密数据。为了平衡负载容量和语义保持之间的冲突，我们提出了一种新的抽样方法，称为双参数语义控制抽样，以切断低概率词。实验结果表明，我们的方法可以控制多样性，负载容量，安全性和语义准确性在同一时间。
英文摘要：
Nowadays, social media has become the preferred communication platform for web users but brought security threats. Linguistic steganography hides secret data into text and sends it to the intended recipient to realize covert communication. Compared to edit-based linguistic steganography, generation-based approaches largely improve the payload capacity. However, existing methods can only generate stego text alone. Another common behavior in social media is sending semantically related image-text pairs. In this paper, we put forward a novel image captioning-based stegosystem, where the secret messages are embedded into the generated captions. Thus, the semantics of the stego text can be controlled and the secret data can be transmitted by sending semantically related image-text pairs. To balance the conflict between payload capacity and semantic preservation, we proposed a new sampling method called Two-Parameter Semantic Control Sampling to cutoff low-probability words. Experimental results have shown that our method can control diversity, payload capacity, security, and semantic accuracy at the same time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
