用注意力机制提升隐写图像质量与容量，打破以往难以兼顾的困局。
StegaVision: Enhancing Steganography with Attention Mechanism
- 在编码解码结构中加入通道与空间注意力模块，优化信息嵌入策略。
- 并行使用两种注意力使图像峰值信噪比和结构相似性均提升，容量也增加。
- 适合关注隐写安全性和图像质量的多媒体信息安全研究者。
图像隐写是将秘密信息嵌入图像的技术。深度学习的发展推动了该领域的显著进步，但现有方法常难以平衡图像质量、嵌入容量与安全性。本文提出一种新方法，通过在编码器-解码器架构中引入注意力机制（包括通道注意力和空间注意力模块），系统评估五种配置：(1) 仅通道注意力，(2) 仅空间注意力，(3) 顺序使用通道后空间注意力，(4) 顺序使用空间后通道注意力，(5) 并行使用通道与空间注意力。实验表明，引入注意力机制能增强隐藏信息能力，同时保持图像视觉质量。并行结合两种注意力时，PSNR 和 SSIM 指标均提升，且嵌入容量增加。这突破了以往方法中图像质量与容量之间的权衡关系。结果证明，注意力机制可有效提升图像隐写的隐蔽性。代码已开源：https://github.com/vlgiitr/StegaVision。
原文摘要 · Abstract (English)
Image steganography is the technique of embedding secret information within images. The development of deep learning has led to significant advances in this field. However, existing methods often struggle to balance image quality, embedding capacity, and security. This paper proposes a novel approach to image steganography by enhancing an encoder-decoder architecture with attention mechanisms, specifically focusing on channel and spatial attention modules. We systematically investigate five configurations: (1) channel attention, (2) spatial attention, (3) sequential channel followed by spatial attention, (4) spatial attention followed by channel attention and (5) parallel channel and spatial attention. Our experiments show that adding attention mechanisms improves the ability to embed hidden information while maintaining the visual quality of the images. The increase in the PSNR and SSIM scores shows that using a parallel combination of channel and spatial attention improves image quality and hiding capacity simultaneously. This is in contrast to previous works where there is a tradeoff between them. This study shows that attention mechanisms in image steganography lead to better hiding of secret information. Our code is available at https://github.com/vlgiitr/StegaVision.
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