Intelligent Steganography through Machine Learning-Guided Pixel Selection for APVD

The present study examines how Adaptive Pixel Value Differencing (APVD) can be combined with machine learning to come up with a content-aware intelligent steganography system. Its main aim is to increase the effectiveness of data hiding, invisibility and resiliency, given that the model dynamically optimizes the procedure using machine learning models. The procedure can be described as training a Random Forest classifier to learn ideal pixels segment and have parameters localized on the image features, e.g., variance and texture. The APVD algorithm of secret data insertion is then guided by this model. This is evaluated experimentally on different sets of images (USC-SIPI and BOSSBase) and tested according to Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and the Bit Error Rate (BER) in face of the simulation of the noise and compression attack. The most notable results show that the machine learning-enabled APVD methodology greatly excels the classic APVD, showing an average PSNR gain of 2-4 dB and a decrease in the BER to a maximum of 30 percentages, in the presence of attack conditions. The developed strategy is a major step towards the development of dynamic and intelligent steganography approaches that can be used to establish secure communication in dynamic and resistant digital networks.