A Robust Image Zero-watermarking using Convolutional Neural Networks

Fierro-Radilla, Atoany; Nakano-Miyatake, Mariko; Cedillo-Hernandez, Manuel; Cleofas-Sánchez, L.; Pérez-Meana, Héctor

doi:10.1109/iwbf.2019.8739245

Cited by 44 publications

(24 citation statements)

References 12 publications

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“…Recent research on image watermarking tasks with deep neural networks has emerged [9], [10], [11], [12], but there still exist challenging issues. For example, it is difficult to fully utilize the fitting ability of deep neural networks to automatically learn and generalize both the watermark embedding and extracting processes.…”

Section: Imentioning

confidence: 99%

“…2 illustrates the overall architecture of the proposed scheme with some example images. Given two input spaces that are all the possible inputs of watermark images and cover-images (W and C, respectively), neural network µ θ 1 parameterized by θ 1 is applied to learn a function that encodes W. W f , the encoded space of W, not only enlarges Kandi et al [8] No No Robust to common image processing attacks Multi-bit Vukotic et al [9] Learning extraction Yes Robust to Rotation, JPEG, and Cropping Single-bit Li et al [10] No No No Multi-bit Fierro-Radilla et al [11] No No Robust to common image processing attacks Zero-watermarking Kim et al [18] Assisting extraction No Focus on geometric attacks Template-based watermarking Mun et al [19] Learning W to prepare for the next-step concatenation, but also brings some redundancy, decomposition, and perceivable randomness to help information protection and robustness. Like the embedding process in traditional watermarking where an encoded w is inserted into a feature space of c, in the proposed scheme, an embedder that takes W f , C as inputs and produces the marked-image is fit by the neural network σ θ 2 parameterized by θ 2 .…”

Section: A the Overview Architecture Of The Proposed Schemementioning

confidence: 99%

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An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks

Zhong

Huang

Mastorakis

et al. 2021

IEEE Trans. Multimedia

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Digital image watermarking is the process of embedding and extracting a watermark covertly on a cover-image. To dynamically adapt image watermarking algorithms, deep learning-based image watermarking schemes have attracted increased attention during recent years. However, existing deep learning-based watermarking methods neither fully apply the fitting ability to learn and automate the embedding and extracting algorithms, nor achieve the properties of robustness and blindness simultaneously. In this paper, a robust and blind image watermarking scheme based on deep learning neural networks is proposed. To minimize the requirement of domain knowledge, the fitting ability of deep neural networks is exploited to learn and generalize an automated image watermarking algorithm. A deep learning architecture is specially designed for image watermarking tasks, which will be trained in an unsupervised manner to avoid human intervention and annotation. To facilitate flexible applications, the robustness of the proposed scheme is achieved without requiring any prior knowledge or adversarial examples of possible attacks. A challenging case of watermark extraction from phone camera-captured images demonstrates the robustness and practicality of the proposal. The experiments, evaluation, and application cases confirm the superiority of the proposed scheme.

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Section: Imentioning

confidence: 99%

Section: A the Overview Architecture Of The Proposed Schemementioning

confidence: 99%

An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks

Zhong

Huang

Mastorakis

et al. 2021

IEEE Trans. Multimedia

View full text Add to dashboard Cite

show abstract

“…Recently the deep learning-based image watermarking became popular to achieve high capacity and robustness of the watermarking systems [27][28][29]. The synergetic neural networks based digital image watermarking has proposed in [27] to ensure the security and robustness of the watermarking system.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient low bit rate image watermarking and tamper detection for image authentication

Kabir

2021

SN Appl. Sci.

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This study presents the vulnerability of digital documents and its effective way to protect the ownership and detection of unauthorized modification of multimedia data. Watermarking is an effective way to protect vulnerable data in a digital environment. In this paper, a watermarking algorithm has been proposed based on a lossy compression algorithm to ensure authentication and detection of forgery. In this proposed method, the CDF9/7 biorthogonal wavelet is used to transform the watermark image and encoded the wavelet coefficients using Set Partition in Hierarchical Tree algorithm. Then, the encoded bits are encrypted by shuffling and encrypting using symmetric keys. After that the encrypted bits are inserted into the Least Significant Bit position of the cover image. In addition, two tamper detection bits are generated based on texture information and pixel location and inserted in the watermarked image. The proposed algorithm reconstructs the watermark and the tampering region more efficiently and achieved 56.5463 dB PSNR for STARE database. Experimental result shows that the proposed algorithm is effectively prevented different attacks and ensure the integrity of watermark bits within the watermarked image. Also finds the tampered region more efficiently compared with the existing state of art algorithms.

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“…On the security side, in various watermarking studies previously proposed encryption techniques on the watermark before being embedded. Watermarks can be encrypted by a variety of methods, from the simplest only with XOR operations, then to DES, RC4, or AES [19][20][21], and currently the most popular is the scramble technique based chaotic-map [22,23]. Scramble technique is more suitable for encrypting multimedia data such as images because it is resistant to differential and statistical attacks, as well as minimizing the possibility of over computing due to the complexity of calculations [24,25].…”

Section: Introductionmentioning

confidence: 99%

An Image Watermarking Method Using Discrete Tchebichef Transform and Singular Value Decomposition Based on Chaos Embedding

Setyono¹,

Setiadi²

2020

IJIES

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A good watermarking method should be robust against attacks but have good visual quality. This research proposes a combination of two transformations i.e. Tchebichef and singular value decomposition (SVD). Arnold's algorithm is also integrated to improve the security of the watermark and visual quality of the watermarked image. Arnold's transformation was carried out on the cover image with the aim to spread watermark. Furthermore, the cover image is divided into small blocks and transformed using Tchebichef. The lowest coefficient of each Tchebichef block is collected in a matrix for decomposition with SVD, then the watermark is embedded in the singular matrix. Based on the results of testing the proposed method can have excellent visual quality with PSNR values ranging from 45dB and SSIM averaging 0.998. The proposed method is also robust to various attacks such as filtering, noise addition, geometry, and signal processing.

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A Robust Image Zero-watermarking using Convolutional Neural Networks

Cited by 44 publications

References 12 publications

An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks

An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks

An efficient low bit rate image watermarking and tamper detection for image authentication

An Image Watermarking Method Using Discrete Tchebichef Transform and Singular Value Decomposition Based on Chaos Embedding

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