Geometric Attacks on Image Watermarking Systems

Licks, V.; Jordan, Ramiro

doi:10.1109/mmul.2005.46

Cited by 115 publications

(49 citation statements)

References 20 publications

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“…Lastly, Geometric attack is a set of parameters that can be applied on the image. There are many types of geometric attacks such as rotation, cropping, and other transformations [21].…”

Section: Watermark Attacksmentioning

confidence: 99%

Properties of digital image watermarking

Abdullatif

Zeki

Chebil

et al. 2013

2013 IEEE 9th International Colloquium on Signal Processing and Its Applications

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Abstract-Digital image watermarking techniques have been developed widely in recent years to maintain the broadcasting media and content authentication, broadcast monitoring, copy control, and many other applications. Therefore, many studies have used digital image watermarking to solve these problem. This paper highlights digital image watermarking. It starts with a basic model of digital image watermarking, it discusses the main requirements and applications. Moreover, it reviews some of the techniques and algorithm used in image watermarking. In addition, digital image watermarking attacks are discussed. Lastly, Watermarking evaluation system is described.

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“…Lastly, Geometric attack is a set of parameters that can be applied on the image. There are many types of geometric attacks such as rotation, cropping, and other transformations [21].…”

Section: Watermark Attacksmentioning

confidence: 99%

Properties of digital image watermarking

Abdullatif

Zeki

Chebil

et al. 2013

2013 IEEE 9th International Colloquium on Signal Processing and Its Applications

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“…Some of these attacks include resizing, scaling, cropping, LSB filliping, JPEG compressing, adding different types of noise, et. al [3,19].…”

Section: Steganalysismentioning

confidence: 99%

Steganography on RGB Images Based on a “Matrix Pattern” using Random Blocks

Nilizadeh¹,

Nilchi²

2013

IJMECS

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Abstract-In this paper, we describe a novel spatial domain method for steganography in RGB images where a secret message is embedded in the blue layer of certain blocks. In this algorithm, each block first chooses a unique t 1 xt 2 matrix of pixels as a "matrix pattern" for each keyboard character, using the bit difference of neighbourhood pixels. Next, a secret message is embedded in the remaining part of the block, those without any role in the "matrix pattern" selection procedure. In this procedure, each pattern sums up with the blue layer of the image. For increasing the security, blocks are chosen randomly using a random generator. The results show that this algorithm is highly resistant against the frequency and spatial domain attacks including RS, Sample pair, X 2 and DCT based attacks. In addition, the proposed algorithm could provide more than 84.26 times of capacity comparing with a competitive method. Moreover, the results indicated that stego-image has almost 1.73 times better transparency than the competitive algorithm.

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“…With the increasing availability of digital multimedia data, the integrity verification of image data becomes more and more important [1]- [3]. Digital images distributed through the Internet may suffer from several possible manipulations [4], as illustrative examples shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…To ensure trustworthiness, image copy detection techniques have emerged to search duplicates and forgeries. Traditionally, image copy detection can be achieved via image hashing [1]- [2] or watermarking [3] techniques. Nevertheless, current hashing techniques may be not very robust to some image manipulations while watermarking techniques will suffer from some distortions induced by data embedding.…”

Section: Introductionmentioning

confidence: 99%

Secure SIFT-based sparse representation for image copy detection and recognition

Kang

Hsu

Chen

et al. 2010

2010 IEEE International Conference on Multimedia and Expo

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In this paper, we formulate the problems of image copy detection and image recognition in terms of sparse representation. To achieve robustness, security, and efficient storage of image features, we propose to extract compact local feature descriptors via constructing the basis of the SIFT-based feature vectors extracted from the secure SIFT domain of an image. Image copy detection can be efficiently accomplished based on the sparse representations and reconstruction errors of the features extracted from an image possibly manipulated by signal processing or geometric attacks. For image recognition, we show that the features of a query image can be represented as sparse linear combinations of the features extracted from the training images belonging to the same cluster. Hence, image recognition can also be cast as a sparse representation problem. Then, we formulate our sparse representation problem as an l 1 -minimization problem. Promising results regarding image copy detection and recognition have been verified, respectively, through the simulations conducted on several content-preserving attacks defined in the Stirmark benchmark and Caltech-101 dataset.

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Geometric Attacks on Image Watermarking Systems

Cited by 115 publications

References 20 publications

Properties of digital image watermarking

Properties of digital image watermarking

Steganography on RGB Images Based on a “Matrix Pattern” using Random Blocks

Secure SIFT-based sparse representation for image copy detection and recognition

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