Many existing coverless steganography methods establish a mapping relationship between cover images and hidden data. There exists an issue that the number of images stored in the database grows exponentially as the steganographic capacity rises. The need for a high steganographic capacity makes it challenging to build an image database. To improve the image library utilization and anti-attack capability of the steganography system, we present an efficient coverless scheme based on dynamically matched substrings. YOLO is employed for selecting optimal objects, and a mapping dictionary is established between these objects and scrambling factors. With the aid of this dictionary, each image is effectively assigned to a specific scrambling factor, which is used to scramble the receiver's sequence key. To achieve sufficient steganography capability based on a limited image library, all substrings of the scrambled sequences hold the potential to hide data. After completing the secret information matching, the ideal number of stego images will be obtained from the database. According to experimental results, this technology outperforms most previous works on data load, transmission security, and hiding capacity. Under typical geometric attacks, it can recover 79.85\% of secret information on average. Furthermore, only approximately 200 random images are needed to meet a capacity of 19 bits per image.

翻译：现有许多无载体隐写方法在载体图像与隐藏数据之间建立映射关系，但存在一个问题：随着隐写容量的增加，数据库中存储的图像数量呈指数级增长。高隐写容量的需求使得构建图像数据库面临挑战。为提高隐写系统的图像库利用率和抗攻击能力，我们提出了一种基于动态匹配子串的高效无载体方案。采用YOLO选取最优目标，并在这些目标与置乱因子之间建立映射字典。借助该字典，每幅图像被有效分配至特定置乱因子，用于置乱接收方的序列密钥。基于有限的图像库实现足够的隐写能力，所有置乱序列的子串均具备隐藏数据的潜力。完成秘密信息匹配后，将从数据库中获取理想的载体图像数量。实验结果表明，该技术在数据载荷、传输安全性和隐藏容量方面优于大部分现有工作。在典型几何攻击下，平均可恢复79.85%的秘密信息。此外，仅需约200张随机图像即可满足每张图像19比特的容量需求。