  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.
