利用视觉聚焦提升隐写容量，达500比特且误码率极低。
Foveation Improves Payload Capacity in Steganography
- 基于视觉焦点设计感知模型，优化隐写信息嵌入。
- 隐写容量从100提升至500比特，测试200K位仅错1位。
- 适合对隐蔽性与视觉质量要求高的水印应用。
隐写术在图像等视觉媒介中用于嵌入元数据和水印。借助高效的潜在表示与视觉聚焦渲染，我们训练出的模型将隐写容量从100比特提升至500比特，在200,000位测试中误差不超过1比特，准确率达99.95%。同时，保持31.47 dB PSNR与0.13 LPIPS的视觉质量，验证了新型多模态潜在表示在隐写中的有效性。
原文摘要 · Abstract (English)
Steganography finds its use in visual medium such as providing metadata and watermarking. With support of efficient latent representations and foveated rendering, we trained models that improve existing capacity limits from 100 to 500 bits, while achieving better accuracy of up to 1 failure bit out of 2000, at 200K test bits. Finally, we achieve a comparable visual quality of 31.47 dB PSNR and 0.13 LPIPS, showing the effectiveness of novel perceptual design in creating multi-modal latent representations in steganography.
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