StegaVision: Enhancing Steganography with Attention Mechanism

Our study, StegaVision, aims to enhance image steganography by integrating attention mechanisms into an autoencoder-based model. Our approach focuses on dynamically adjusting the importance of different parts of the image through attention mechanisms. This helps in better embedding the hidden information while maintaining the image’s visual quality. We specifically explore two types of attention mechanisms—Channel Attention and Spatial Attention—and test their effectiveness on an autoencoder model.