A New Robust Color Image Watermarking Scheme Based on 3D-DCT

Xiong, Xiangguang

doi:10.4236/wjet.2015.33c026

Cited by 10 publications

(2 citation statements)

References 9 publications

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“…Traditional 3D models are mainly represented in the form of point clouds Zhang Guoguo and Liu (2023), voxels Li Baoyang and Zhang (2023), or triangular meshes Jian and Guoguo (2023), and are usually embedded with watermarks by translating, rotating, scaling, and other operations on the 3D shape Xiang-Guang et al (2015) or modifying the 3D model parameters to embed watermarks Pham et al (2018). In 2017, Hou et al (2017) proposed a method for 3D watermarking using layered artifacts of 3D printed objects.…”

Section: Digital Watermarking For 3dmentioning

confidence: 99%

NeRF in NeRF: An Implicit Representation Watermark Algorithm for NeRF

Chen,

Liu,

Sun

et al. 2024

Preprint

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The Neural Radiation Field (NeRF) has achieved significant results in the field of computer vision. However, training NeRF models requires a large amount of computing resources and training data. Once the model is stolen or abused by illegal users, it may cause huge losses to copyright owners. This article proposes a watermarking framework for copyright protection using Implicit Neural Representation (INR). After implicitly representing the watermark information, we embed the network representing the watermark information into the network of the NeRF model by designing a specific key. The copyright verifier extracts the watermark from the NeRF model using the key to prove the copyright of the NeRF model. To our knowledge, this is the first time INR has been introduced into the copyright protection of the NeRF model. The experimental results show that our method not only has high robustness and maintains good 3D reconstruction quality, but also achieves lossless (100%) extraction of watermark information, effectively protecting the copyright of the NeRF model.

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Section: Digital Watermarking For 3dmentioning

confidence: 99%

NeRF in NeRF: An Implicit Representation Watermark Algorithm for NeRF

Chen,

Liu,

Sun

et al. 2024

Preprint

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“…The technique ensures the confidentiality of the data and shows stronger robustness against different attacks compared with Refs. 77–82. However, a transparent investigation of security and cost is required.…”

Section: State-of-the-art Watermarking In Deep-learning Environmentsmentioning

confidence: 99%

Comprehensive review of watermarking techniques in deep-learning environments

Singh

2022

J. Electron. Imag.

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Recently, the demand for the generation, sharing, and storage of massive amounts of multimedia information-especially in the form of images-from different intelligent devices and sensors has increased drastically. This introduces issues including the illegal access and fraudulent usage of this information as well as other security concerns. Watermarking consists of embedding a watermark design in a digital cover and then later extracting it to provide a solution for ownership conflict and copyright violation issues involving the media data. Presently, in watermarking, the use of deep-learning approaches is incredibly beneficial due to their accuracy, superior results and strong learning ability. We present a comprehensive review of watermarking techniques in deep-learning environments. We start with basic concepts of traditional and learning-based digital watermarking; we later review the popular deep-learning model-based digital watermarking methods; then, we summarize and compare the most recent contribution in the literature; finally, we highlight obfuscation challenges and further research directions.

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