# BASN -- Learning Steganography with Binary Attention Mechanism

## TL;DR

This paper introduces a binary attention mechanism for image steganography, enhancing security and payload capacity while resisting detection, by maintaining feature map integrity against neural network-based steganalysis.

## Contribution

It proposes a novel binary attention mechanism that improves security and increases embedding payload in image steganography, addressing neural network detection challenges.

## Key findings

- High payload capacity achieved with minimal feature distortion
- Resists detection by state-of-the-art steganalysis algorithms
- Maintains feature map integrity against neural network-based analysis

## Abstract

Secret information sharing through image carrier has aroused much research attention in recent years with images' growing domination on the Internet and mobile applications. However, with the booming trend of convolutional neural networks, image steganography is facing a more significant challenge from neural-network-automated tasks. To improve the security of image steganography and minimize task result distortion, models must maintain the feature maps generated by task-specific networks being irrelative to any hidden information embedded in the carrier. This paper introduces a binary attention mechanism into image steganography to help alleviate the security issue, and in the meanwhile, increase embedding payload capacity. The experimental results show that our method has the advantage of high payload capacity with little feature map distortion and still resist detection by state-of-the-art image steganalysis algorithms.