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StegaINR4MIH: steganography by implicit neural representation for multi-image hiding

Autor: Dong, Weina, Liu, Jia, Chen, Lifeng, Sun, Wenquan, Pan, Xiaozhong, Ke, Yan

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 a

Externí odkaz: http://arxiv.org/abs/2410.10117

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Watermarking for Neural Radiation Fields by Invertible Neural Network

Autor: Sun, Wenquan, Liu, Jia, Dong, Weina, Chen, Lifeng, Niu, Ke

To protect the copyright of the 3D scene represented by the neural radiation field, the embedding and extraction of the neural radiation field watermark are considered as a pair of inverse problems of image transformations. A scheme for protecting th

Externí odkaz: http://arxiv.org/abs/2312.02456

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MarkNerf:Watermarking for Neural Radiance Field

Autor: Chen, Lifeng, Liu, Jia, Ke, Yan, Sun, Wenquan, Dong, Weina, Pan, Xiaozhong

A watermarking algorithm is proposed in this paper to address the copyright protection issue of implicit 3D models. The algorithm involves embedding watermarks into the images in the training set through an embedding network, and subsequently utilizi

Externí odkaz: http://arxiv.org/abs/2309.11747

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Steganography for Neural Radiance Fields by Backdooring

Autor: Dong, Weina, Liu, Jia, Ke, Yan, Chen, Lifeng, Sun, Wenquan, Pan, Xiaozhong

The utilization of implicit representation for visual data (such as images, videos, and 3D models) has recently gained significant attention in computer vision research. In this letter, we propose a novel model steganography scheme with implicit neur

Externí odkaz: http://arxiv.org/abs/2309.10503

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