Fractal Based Digital Image Watermarking Using Fuzzy C-Mean Clustering

Kiani, Soheila; Moghaddam, Mohsen Ebrahimi

doi:10.1109/icime.2009.72

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…In the past decades, fractal image coding has mainly been developed for the purpose of image compression [21][22][23][24][25][26][27][28][29][30][31][32][33] as well as some for image denoising [34][35][36][37] and image indexing [38]. Recently, it is also well studied for gray-level image watermarking [39][40][41][42][43]. Davern and Scott [39] described a steganographic method for inserting secret information into images using fractal image coding.…”

Section: Introductionmentioning

confidence: 99%

“…The visual key must then be used when retrieving the hidden data.Candik et al [40] investigated two possible approaches of embedding digital watermarks into fractal codes of images, which are embedding digital watermarks into parameters for position of similar blocks and coefficients of block similarity. [41] presented a watermarking method to embed a binary watermark into an image using a special type of fractal coding that its parameters are contrast scaling and the mean of range block. And it utilized the fuzzy -mean clustering to address the watermark bits.…”

Section: Introductionmentioning

confidence: 99%

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A Robust Fractal Color Image Watermarking Algorithm

Lü

Zou

Yang

et al. 2014

Mathematical Problems in Engineering

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One of the main objectives of watermarking is to achieve a better tradeoff between robustness and high visual quality of a host image. In recent years, there has been a significant development in gray-level image watermarking using fractal-based method. This paper presents a human visual system (HVS) based fractal watermarking method for color images. In the proposed method, a color pixel is considered as a 3-D vector inRGBspace. And a general form of 3 × 3 matrix is utilized as the scaling operator. Meanwhile, the luminance offset vector is substituted by the range block mean vector. Then an orthogonalization fractal color coding method is achieved to obtain very high image quality. We also show that the orthogonalization fractal color decoding is a mean vector-invariant iteration. So, the range block mean vector is a good place for hiding watermark. Furthermore, for consistency with the characteristics of the HVS, we carry out the embedding process in theCIE La*b*space and incorporate a just noticeable difference (JND) profile to ensure the watermark invisibility. Experimental results show that the proposed method has good robustness against various typical attacks, at the same time, with an imperceptible change in image quality.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Robust Fractal Color Image Watermarking Algorithm

Lü

Zou

Yang

et al. 2014

Mathematical Problems in Engineering

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“…Before the actual embedding operation, the cover pixel selection is based on fuzzy pixel classification and the secret message is converted to a mode of fuzzy data. Kiani et al (2009) clarify another fuzzy-based watermarking technique that employs a fuzzy-c clustering algorithm based on transform domain derivative features in various directions. Fuzzy logic can improve steganographic schemes in a variety of ways, particularly when the image textures are vague and/or ambiguous.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Proximity based Robust Data Hiding Scheme with Interval Threshold

Singh

Jana

Datta

2022

Preprint

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In 2020, Ashraf et al. proposed an interval type-2 fuzzy logic based block similarity calculation using color proximity relations of neighboring pixels in a steganographic scheme. Their method works well for detecting similarity, but it has drawbacks in terms of visual quality, imperceptibility, security, and robustness. Using Mamdani fuzzy logic to identify color proximity at the block level, as well as a shared secret key and post-processing system, this paper attempts to develop a robust data hiding scheme with similarity measure to ensure good visual quality, robustness, imperceptibility, and enhance the security. Further, the block color proximity is graded using an interval threshold. Accordingly, data embedding is processed in the sequence generated by the shared secret keys. In order to increase the quality and accuracy of the recovered secret message, the tampering coincidence problem is solved through a post-processing approach. The experimental analysis, steganalysis and comparisons clearly illustrate the effectiveness of the proposed scheme in terms of visual quality, structural similarity, recoverability and robustness.

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“…The Fuzzy C-Mean algorithm was used to cluster data first, then the Fuzzy Inference System had been created based on these clusters by various rules, maximum numbers of inputs and flooded hydraulic conductivity as a result in [12]. Kiani, S. et al [13] proposed a method which uses a special type of fractal coding. Its constraints are contrasted scaling and the average range of block.…”

Section: Related Workmentioning

confidence: 99%

An Edge Exposure using Caliber Fuzzy C-means With Canny Algorithm

Jeyaraman

Janakiraman

2017

IJEECS

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<p>Edge exposure or edge detection is an important and classical study of the medical field and computer vision. Caliber Fuzzy C-means (CFCM) clustering Algorithm for edge detection depends on the selection of initial cluster center value. This endeavor to put in order a collection of pixels into a cluster, such that a pixel within the cluster must be more comparable to every other pixel. Using CFCM techniques first cluster the BSDS image, next the clustered image is given as an input to the basic canny edge detection algorithm. The application of new parameters with fewer operations for CFCM is fruitful. According to the calculation, a result acquired by using CFCM clustering function divides the image into four clusters in common. The proposed method is evidently robust into the modification of fuzzy c-means and canny algorithm. The convergence of this algorithm is very speedy compare to the entire edge detection algorithms. The consequences of this proposed algorithm make enhanced edge detection and better result than any other traditional image edge detection techniques.</p>

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Fractal Based Digital Image Watermarking Using Fuzzy C-Mean Clustering

Cited by 9 publications

References 6 publications

A Robust Fractal Color Image Watermarking Algorithm

A Robust Fractal Color Image Watermarking Algorithm

Fuzzy Proximity based Robust Data Hiding Scheme with Interval Threshold

An Edge Exposure using Caliber Fuzzy C-means With Canny Algorithm

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