Zhong Yangjie | Searcharxiv

arXiv2025

INR-Based Generative Steganography by Point Cloud Representation

Generative steganography (GS) directly generates stego-media through secret message-driven generation. It makes the hiding capacity of GS higher than that of traditional steganography, as well as more resistant to classical steganalysis. However, the generators and extractors of existing GS methods can only target specific formats and types of data and lack of universality. Besides, the model size is usually related to the underlying grid resolution, and the transmission behavior of the extractor is susceptible to suspicion of steganalysis. Implicit neural representation(INR) is a technique for representing data in a continuous manner. Inspired by this, we propose an INR-based generative steganography by point cloud representation (INR-GSPC). By using the function generator, the problem of the generator model size growing exponentially with the increase of gridded data has been solved. That is able to generate a wide range of data types and break through the limitation of resolution. In order to unify the data formats of the generator and message extractor, the data is converted to point cloud representation. We designed and fixed a point cloud message extractor. By iterating over the point cloud with adding small perturbations to generate stego-media. This method can avoid the training and transmission process of the message extractor. To the best of our knowledge, this is the first method to apply point cloud to generative steganography. Experiments demonstrate that the stego-images generated by the scheme have an average PSNR value of more than 65, and the accuracy of message extraction reaches more than 99%.

cs.CR↗

arXiv2024

Image steganography based on generative implicit neural representation

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%.

cs.CR↗