Enhanced payload volume in the least significant bits image steganography using hash function.

The art of message masking is called steganography. Steganography keeps communication from being seen by any other person. In the domain of information concealment within images, numerous steganographic techniques exist. Digital photos stand out as prime candidates due to their widespread availability. This study seeks to develop a secure, high-capacity communication system that ensures private interaction while safeguarding information from the broader context. This study used the four least significant bits for steganography to hide the message in a secure way using a hash function. Before steganography, the message is encrypted using one of the encryption techniques: Caesar cipher or Vigenère cipher. By altering only the least significant bits (LSBs), the changes between the original and stego images remain invisible to the human eye. The proposed method excels in secret data capacity, featuring a high peak signal-to-noise ratio (PSNR) and low mean square error (MSE). This approach offers significant payload capacity and dual-layer security (encryption and steganography).

An efficient optimizer for the 0/1 knapsack problem using group counseling.

The field of optimization is concerned with determining the optimal solution to a problem. It refers to the mathematical loss or gain of a given objective function. Optimization must reduce the given problem's losses and disadvantages while maximizing its earnings and benefits. We all want optimal or, at the very least, suboptimal answers because we all want to live a better life. Group counseling optimizer (GCO) is an emerging evolutionary algorithm that simulates the human behavior of counseling within a group for solving problems. GCO has been successfully applied to single and multi-objective optimization problems. The 0/1 knapsack problem is also a combinatorial problem in which we can select an item entirely or drop it to fill a knapsack so that the total weight of selected items is less than or equal to the knapsack size and the value of all items is as significant as possible. Dynamic programming solves the 0/1 knapsack problem optimally, but the time complexity of dynamic programming is O(n3). In this article, we provide a feature analysis of GCO parameters and use it to solve the 0/1 knapsack problem (KP) using GCO. The results show that the GCO-based approach efficiently solves the 0/1 knapsack problem; therefore, it is a viable alternative to solving the 0/1 knapsack problem.