BASN -- Learning Steganography with Binary Attention Mechanism
This work addresses security issues in image steganography for applications like secret information sharing, though it appears incremental as it builds on existing methods with a novel mechanism.
The paper tackles the challenge of improving security and payload capacity in image steganography against neural-network-based detection, introducing a binary attention mechanism that achieves high payload capacity with minimal distortion and resistance to state-of-the-art steganalysis.
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.