Digital watermarking of color images utilizing mobile platforms

Delgado-Guillen, Lorenzo Antonio; García-Hernández, José Juan; Torres-Huitzil, César

doi:10.1109/mwscas.2013.6674909

Cited by 22 publications

(19 citation statements)

References 5 publications

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“…The challenges in this use-case arise because watermark extraction needs to handle multiple combinations of the distortions, such as optical tilt, quality degradation, compression, lens distortions, and lighting variation. Most existing approaches [29], [30], [31], [32] focus on the resamples of printed marked-images, not on phone resamples of a computer monitor screen. This usecase typically involves additional distortions, such as the Moiré pattern (i.e., the RGB ripple), the refresh rate of the screen, and the spatial resolution of a monitor (some examples are mentioned in Fig.…”

Section: E a Case Study: Feasibility Test On Watermark Extraction Frmentioning

confidence: 99%

“…Classic Reed Solomon (RS) code [35] is adopted as the ECC to protect the information. RS (32,16) is applied to protect each row of the 32 × 16 information so that the encoded information will be a 32 × 32 watermark satisfying the fixed watermarking capacity of the proposed scheme. In the watermark, each row is a codeword with data of length 16 and a parity of length 16, and hence can correct up to an error of length 8.…”

Section: E a Case Study: Feasibility Test On Watermark Extraction Frmentioning

confidence: 99%

“…For a visual comparison, the displayed watermark extractions are the raw results before error correction. After executing RS (32,16), all the watermark extractions in the testing cases can be restored to the original information without errors, as shown in Fig. 14.…”

Section: E a Case Study: Feasibility Test On Watermark Extraction Frmentioning

confidence: 99%

See 2 more Smart Citations

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

Zhong

Huang

Mastorakis

et al. 2021

IEEE Trans. Multimedia

103

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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: E a Case Study: Feasibility Test On Watermark Extraction Frmentioning

confidence: 99%

Section: E a Case Study: Feasibility Test On Watermark Extraction Frmentioning

confidence: 99%

See 1 more Smart Citation

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

Zhong

Huang

Mastorakis

et al. 2021

IEEE Trans. Multimedia

103

View full text Add to dashboard Cite

show abstract

“…All these methods operate in the spatial domain and only a few have proposed methods in other domains. Delgado-Guillen et al (2013) operated their method in log-polar transformations and Pramila et al (2008) divided the method across several domains.…”

Section: Preprint Submitted To Elseviermentioning

confidence: 99%

Increasing the capturing angle in print-cam robust watermarking

Pramila

Keskinarkaus

Seppänen

2018

Journal of Systems and Software

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It is nowadays more probable that a print media is captured and shared with a mobile phone than with a scanner. The reasons for photographing the print range from intention of copying the image to simply sharing an interesting add with friends. Watermarking offers a solution for carrying side information in the images, and if the watermarking method being used is robust to the print-cam process, the information can be read with a mobile phone camera. In this paper, we present a print-cam robust watermarking method that is also implemented on a mobile phone and evaluated with user tests. Especially, the lens focusing problem when the picture is captured in a wide angle with respect to the printout is addressed. The results show that the method is highly robust to capturing the watermark without errors in angles up to 60➦ with processing times that are acceptable for real-life applications.

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“…Few have researched other domains. Delgado-Guillen et al [6] utilized log-polar transformations whereas Pramila et al [19] opted to divide the watermark into several domains using multiple watermarks.…”

Section: Related Workmentioning

confidence: 99%

Extracting watermarks from printouts captured with wide angles using computational photography

Pramila

Keskinarkaus

Takala

et al. 2016

Multimed Tools Appl

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Thus far the research of print-cam robust watermarking methods has focused on finding new methods for embedding and extracting the watermark. However, the capturing process itself, has been neglected in scientific research. In this paper, we propose a solution for the situation when the watermarked image has been captured in a wide angle and the depth of focus of the camera is not deep enough to capture the whole scene in-focus resulting in unfocused areas. The solution proposed here relies on a subfield of computational photography, namely all-in-focus imaging. All-in-focus images are generated by fusing multiple images from the same scene with different focus distances together, so that the object being photographed is fully in focus. Traditionally, the images to be fused are selected by hand from the focal stack or the whole stack is used for building the all-in-focus image. In mobile phone applications, computational resources are limited and using the full focal stack would result in long processing times and the manual selection of images would not be practical. In addition, we propose a method for optimizing the size of the focal stack and automatically selecting appropriate images for fusion. It is shown here that a watermark can still be recovered from the reconstructed all-in-focus image accurately.

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Digital watermarking of color images utilizing mobile platforms

Cited by 22 publications

References 5 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

Increasing the capturing angle in print-cam robust watermarking

Extracting watermarks from printouts captured with wide angles using computational photography

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