Improving Adaptive Steganography Performance with Intelligent Embedding Key Selection Using Optimization Algorithms

Adaptive steganography methods use variable embedding capacity according to the uniformity or edges of image areas. ALSBMR is an adaptive method with two main stages: Selecting suitable pixels, and embedding them using the LSBMR method. This method utilizes two adaptive keys between the sender and the receiver to determine the block rotation angle and select the embedding path. In the original method, the keys are randomly selected by the sender with no specific criteria and then sent to the receiver. The proposed method models key selection as an optimization problem and uses Genetic Algorithm (GA) and Teaching-Learning-Based Optimization (TLBO) to find the optimal keys. Two fitness functions are used to further evaluate the difference as well as the histogram difference between the cover and stego images. The results show that the image embedded with the proposed method has improved quality and security compared to the base method. Since all steganography methods require embedding keys, intelligent key selection can improve the performance of existing steganography methods.