In the realm of advanced steganography, the scale of the model typically correlates directly with the resolution of the fundamental grid, necessitating the training of a distinct neural network for message extraction. This paper proposes an image steganography based on generative implicit neural representation. This approach transcends the constraints of image resolution by portraying data as continuous functional expressions. Notably, this method permits the utilization of a diverse array of multimedia data as cover images, thereby broadening the spectrum of potential carriers. Additionally, by fixing a neural network as the message extractor, we effectively redirect the training burden to the image itself, resulting in both a reduction in computational overhead and an enhancement in steganographic speed. This approach also circumvents potential transmission challenges associated with the message extractor. Experimental findings reveal that this methodology achieves a commendable optimization efficiency, achieving a completion time of just 3 seconds for 64x64 dimensional images, while concealing only 1 bpp of information. Furthermore, the accuracy of message extraction attains an impressive mark of 100%.

翻译：在高级隐写术领域，模型规模通常与基础网格的分辨率直接相关，这需要为消息提取训练独立的神经网络。本文提出一种基于生成式隐式神经表示的图像隐写方法。该方法通过将数据描述为连续函数表达式，突破了图像分辨率的限制。值得注意的是，此方法允许使用多种多媒体数据作为载体图像，从而拓宽了潜在载体的范围。此外，通过固定神经网络作为消息提取器，我们将训练负担有效转移至图像本身，既降低了计算开销，又提升了隐写速度。该方法还规避了与消息提取器相关的潜在传输难题。实验结果表明，该方法实现了可观的优化效率，在仅隐藏1 bpp信息的情况下，64x64维度图像的处理时间仅需3秒。此外，消息提取的准确率达到了令人瞩目的100%。