Digital watermarking using Hall property image decomposition method

Muhammad, Nazeer; Bibi, Nargis; Qasim, Iqbal; Jahangir, Adnan; Mahmood, Zahid

doi:10.1007/s10044-017-0613-z

Cited by 33 publications

(19 citation statements)

References 30 publications

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“…For a thorough treatment of such matters, the reader is invited to consult the excellent survey [55]. In general, low rank matrix factorizations find utility in a broad variety of applications, including copyright security [56], [57], imaging [58], and matrix completion [59] to name a very few.…”

Section: Cur Decompositionmentioning

confidence: 99%

CUR Decompositions, Similarity Matrices, and Subspace Clustering

Aldroubi

Hamm

Koku

et al. 2019

Front. Appl. Math. Stat.

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A general framework for solving the subspace clustering problem using the CUR decomposition is presented. The CUR decomposition provides a natural way to construct similarity matrices for data that come from a union of unknown subspaces U = M i=1 S i . The similarity matrices thus constructed give the exact clustering in the noise-free case. Additionally, this decomposition gives rise to many distinct similarity matrices from a given set of data, which allow enough flexibility to perform accurate clustering of noisy data. We also show that two known methods for subspace clustering can be derived from the CUR decomposition. An algorithm based on the theoretical construction of similarity matrices is presented, and experiments on synthetic and real data are presented to test the method.Additionally, an adaptation of our CUR based similarity matrices is utilized to provide a heuristic algorithm for subspace clustering; this algorithm yields the best overall performance to date for clustering the Hopkins155 motion segmentation dataset.

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Section: Cur Decompositionmentioning

confidence: 99%

CUR Decompositions, Similarity Matrices, and Subspace Clustering

Aldroubi

Hamm

Koku

et al. 2019

Front. Appl. Math. Stat.

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“…In above C andĆ represent the encrypted images obtained as a result of single bit change in the original image. In (4) and (5), W and H represent the width and the height of the images C andĆ.…”

Section: Differential Analysismentioning

confidence: 99%

An Image Encryption Technique based on Chaotic S-Box and Arnold Transform

Farwa¹,

Shah²,

Muhammad³

et al. 2017

ijacsa

Self Cite

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In recent years, chaos has been extensively used in cryptographic systems. In this regard, one dimensional chaotic maps gained increased attention because of their intrinsic simplicity and ease in application. Many image encryption algorithms that are based on chaotic substitution boxes (S-boxes) have been studied in the last few years but some of them appear to be vulnerable to robustness. We, in this paper, propose an efficient scheme for image encryption that utilizes a composition of chaotic substitution based on tent map with the scrambling effect of the Arnold transform. The proposed construction algorithm for substitution box is, on one hand, straightforward and saves computational labour, while on the other, it provides highly efficient performance outcomes. The understudy scheme uses an S-box, that is based on 1-D chaotic tent map. We partially encrypt the image using this S-box and then apply certain number of iterations of the Arnold transform to attain the fully encrypted image. For decryption we apply the reverse process. The strength of the proposed method is determined through the most significant techniques used for the statistical analysis and it is proved that the anticipated algorithm shows coherent results.

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“…The manifestation of the Internet and technology caused the growth of data in multiple forms as video, image, audio, text, etc. [29,30]. The key is how to integrate them to benefit complementary information from each view.…”

Section: Introductionmentioning

confidence: 99%

Multi Similarity Metric Fusion in Graph-Based Semi-Supervised Learning

2019

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In semi-supervised label propagation (LP), the data manifold is approximated by a graph, which is considered as a similarity metric. Graph estimation is a crucial task, as it affects the further processes applied on the graph (e.g., LP, classification). As our knowledge of data is limited, a single approximation cannot easily find the appropriate graph, so in line with this, multiple graphs are constructed. Recently, multi-metric fusion techniques have been used to construct more accurate graphs which better represent the data manifold and, hence, improve the performance of LP. However, most of these algorithms disregard use of the information of label space in the LP process. In this article, we propose a new multi-metric graph-fusion method, based on the Flexible Manifold Embedding algorithm. Our proposed method represents a unified framework that merges two phases: graph fusion and LP. Based on one available view, different simple graphs were efficiently generated and used as input to our proposed fusion approach. Moreover, our method incorporated the label space information as a new form of graph, namely the Correlation Graph, with other similarity graphs. Furthermore, it updated the correlation graph to find a better representation of the data manifold. Our experimental results on four face datasets in face recognition demonstrated the superiority of the proposed method compared to other state-of-the-art algorithms.

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Digital watermarking using Hall property image decomposition method

Cited by 33 publications

References 30 publications

CUR Decompositions, Similarity Matrices, and Subspace Clustering

CUR Decompositions, Similarity Matrices, and Subspace Clustering

An Image Encryption Technique based on Chaotic S-Box and Arnold Transform

Multi Similarity Metric Fusion in Graph-Based Semi-Supervised Learning

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