With the rapid development of multimedia technology and computer networks, confidential and sensitive digital information is frequently transmitted over the Internet. To pre vent this information from being intentionally damaged or forged, ensuring its security during transmission has become an important research topic. Coverless image steganography can hide secret messages within images for transmission without leaving any traces, making it one of the main methods considered for securely conveying secret information. However, existing cover less image steganography techniques overlook the possibility that the number of images in the dataset may be insufficient to con struct a complete image sequence index, rendering them impractical for data hiding applications. In light of this, this paper pro poses a novel coverless image steganography technique based on multi-object detection. By creating indexes based primarily on object labels contained within images, it establishes a flexible mapping relationship between secret message segments and im age feature sequences, thereby reducing the number of images required to construct a complete sequence index. Experimental results demonstrate that this method can effectively perform the task of hiding secret messages in real image datasets. Further-more, our method significantly outperforms previous research in terms of steganographic capacity and robustness, showing substantial improvements of 27.65% and 16.07%, respectively.

In this paper, a problem of production plans, named k-most demanding products (k-MDP) discovering, is formulated. Given a set of customers demanding a certain type of products with multiple attributes, a set of existing products of the type, a set of candidate products that can be offered by a company, and a positive integer k, we want to help the company to select k products from the candidate products such that the expected number of the total customers for the k products is maximized. We show the problem is NP-hard when the number of attributes for a product is 3 or more. One greedy algorithm is proposed to find approximate solution for the problem. We also attempt to find the optimal solution of the problem by estimating the upper bound of the expected number of the total customers for a set of k candidate products for reducing the search space of the optimal solution. An exact algorithm is then provided to find the optimal solution of the problem by using this pruning strategy. The experiment results demonstrate that both the efficiency and memory requirement of the exact algorithm are comparable to those for the greedy algorithm, and the greedy algorithm is well scalable with respect to k.