Graph Spectral Domain Blind Watermarking

Al-Khafaji, Hiba; Abhayaratne, Charith

doi:10.1109/icassp.2019.8683753

Cited by 7 publications

(6 citation statements)

References 19 publications

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“…The algorithm offered better robustness against common signal-processing operations; however, it has not been tested against any hybrid attacks. Khafaji et al [9] proposed a robust watermarking algorithm by using selected graph Fourier coefficients to embed a watermark. The proposed scheme demonstrated improved robustness against various attacks by establishing a relationship between watermark extraction.…”

Section: Related Researchmentioning

confidence: 99%

Spatial Domain-Based Robust Watermarking Framework for Cultural Images

et al. 2022

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Heritage multimedia, which include photographs, customs, knowledge, arts, rituals, audio, cultural information, and music, are valuable artifacts of any region. The most important attribute of heritage media is the transmission of important features of past generations, which reflect their way of living, innovative attitude, and diversity in archaeological and historical perspectives. However, the proliferation of the Internet has made such data exchange more challenging than ever, allowing unauthorized users to easily access such information. Under such circumstances, securing cultural heritage (CH) media is essential. In that regard, herein, we present a spatial domain-based blind and robust watermarking scheme for the ownership verification of colored CH images; this scheme uses DC coefficient modification. In this scheme, the "Y" element of the YCbCr space is used for inserting a watermark. The "Y" element of a host image is divided into non-overlapping blocks with sizes of 8 × 8. Each 8 × 8 block is then divided into two 4 × 8 subblocks. Instead of calculating the DC coefficients using the discrete cosine transform, we independently calculate the DC coefficient of every 4 × 8 subblock in the spatial domain. We test our method based on standard test images obtained from the USC-SIPI dataset and a self-created dataset of cultural images. Our scheme demonstrates improved robustness and lower computational complexity than frequency-domainbased techniques. The average peak signal-to-noise ratio of the proposed technique for test images is 40.0830 dB, and the structural similarity index matrix value is closer to one under no attack, ensuring the imperceptibility of the technique. Further, we prove the resilience of the proposed algorithm by comparing it with various state-of-the-art techniques.

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Section: Related Researchmentioning

confidence: 99%

Spatial Domain-Based Robust Watermarking Framework for Cultural Images

et al. 2022

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“…This requires us to hiding information into graph data without impairing the value of the graph data. One can extend conventional media based algorithms to graph data, e.g., [23]. On the other hand, similar to graph steganography in social networks, secret data can be first translated as graphs and then embedded into the host graph data, e.g., [24].…”

Section: Other Graph Strategies In Information Hidingmentioning

confidence: 99%

Graph Models in Information Hiding

Wu¹

2022

Recent Applications in Graph Theory

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Information hiding allows us to hide secret information into digital objects such as images without significantly distorting the objects. The object containing hidden information will be transmitted to a data receiver via a probably insecure channel. To securely transmit the object carrying hidden information, the distortion caused by data embedding should be as low as possible, which is referred to as the rate-distortion optimization problem. Many conventional methods optimize the data embedding procedure by a heuristic fashion, which may be not optimal in terms of the rate-distortion performance. In this chapter, we introduce novel approaches that use graph theory for information hiding. These graph models are general and can be used for improving the rate-distortion performance of information hiding systems. In addition to rate-distortion optimization, recent graph models used for system design of information hiding will be also reviewed. This chapter is intended as a tutorial introducing advanced graph models applied to information hiding.

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“…Even so the 3D objects are widely available and important, there are a few existing watermarking techniques. e various watermarking techniques for 3D objects can be classified according to the embedding domains such as the spatial domain [18,19], the spectral-domain [20,21], and the transform domain [22,23].…”

Section: Introductionmentioning

confidence: 99%

“…Even so the 3D objects are widely available and important, there are a few existing watermarking techniques. The various watermarking techniques for 3D objects can be classified according to the embedding domains such as the spatial domain [ 18 , 19 ], the spectral-domain [ 20 , 21 ], and the transform domain [ 22 , 23 ]. The transform domain techniques such as Fourier, Laplace, cosine, and wavelet transform provide a good trade-off between invisibility and robustness.…”

Section: Introductionmentioning

confidence: 99%

A Blind Watermarking Model of the 3D Object and the Polygonal Mesh Objects for Securing Copyright

Al-Saadi

Ghareeb

Elhadad

2021

Computational Intelligence and Neuroscience

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In this paper, we propose a novel model for 3D object watermarking. The proposed method is based on the properties of the discrete cosine transform (DCT) of the 3D object vertices to embed a secret grayscale image three times. The watermarking process takes place by using the vertices coefficients and the encrypted image pixels. Moreover, the extraction process is totally blind based on the reverse steps of the embedding process to recover the secret grayscale image. Various performance aspects of the method are measured and compared between the original 3D object and the watermarked one using Euclidean distance, Manhattan distance, cosine distance, and correlation distance. The obtained results show that the proposed model provides better performances in terms of execution time and invisibility.

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Graph Spectral Domain Blind Watermarking

Cited by 7 publications

References 19 publications

Spatial Domain-Based Robust Watermarking Framework for Cultural Images

Spatial Domain-Based Robust Watermarking Framework for Cultural Images

Graph Models in Information Hiding

A Blind Watermarking Model of the 3D Object and the Polygonal Mesh Objects for Securing Copyright

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