隐写术
计算机科学
信息隐藏
人工智能
图像处理
代表(政治)
计算机视觉
图像(数学)
人工神经网络
模式识别(心理学)
政治
政治学
法学
作者
Weina Dong,Jia Liu,Lifeng Chen,Wenquan Sun,Xiaozhong Pan,Yan Ke
标识
DOI:10.1117/1.jei.33.6.063017
摘要
Multi-image hiding, which embeds multiple secret images into a cover image and is able to recover these images with high quality, has gradually become a research hotspot in the field of image steganography. However, due to the need to embed a large amount of data in a limited cover image space, issues such as contour shadowing or color distortion often arise, posing significant challenges for multi-image hiding. We propose StegaINR4MIH, a implicit neural representation steganography framework that enables the hiding of multiple images within a single implicit representation function. In contrast to traditional methods that use multiple encoders to achieve multi-image embedding, our approach leverages the redundancy of implicit representation function parameters and employs magnitude-based weight selection and secret weight substitution on pre-trained cover image functions to effectively hide and independently extract multiple secret images. We conduct experiments on images with a resolution from three different datasets: CelebA-HQ, COCO, and DIV2K. When hiding two secret images, the PSNR values of both the secret images and the stego images exceed 42. When hiding five secret images, the PSNR values of both the secret images and the stego images exceed 39. Extensive experiments demonstrate the superior performance of the proposed method in terms of visual quality and undetectability.
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