Additive Watermark Detectors Based on a New Hierarchical Spatially Adaptive Image Model

Mairgiotis, Antonis; Galatsanos, Nikolaos; Yang, Yongi

doi:10.1109/icip.2007.4379865

Cited by 8 publications

(25 citation statements)

References 32 publications

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“…The statistical tests of the GGD and Cauchy distribution are presented in Table II. [19] − − − − − Cheng and Huang [20] − − − − − − Nikolaidis and Pitas [21] − − − − − − Zeng and Liu [22] −…”

Section: Introductionunclassified

Optimum watermark detection of ultrasonic echo medical images

Khawne

Attachoo

Hamamoto

2014

IEEJ Transactions Elec Engng

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To prevent unauthorized access to a person's medical images, it is widely acknowledged that the security features of confidentiality, availability and integrity should be in place. This paper proposes the watermarking of ultrasonic echo images together with optimal watermark detection, in which pseudorandom noise is added to the images for integrity. The optimum watermark detection is the integration of the generalized Gaussian distribution (ρ‐GGD) and the Cauchy distribution. The results show that the proposed method gives good detection performance. The proposed method not merely achieves optimum detection using the Rao test but also leads to the highest detection probability with JPEG2000 compression. Compared with other detection methods, our proposed method exhibits better watermark detection performance even when the watermark‐to‐document ratio (WDR) is −50 dB. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Introductionunclassified

Optimum watermark detection of ultrasonic echo medical images

Khawne

Attachoo

Hamamoto

2014

IEEJ Transactions Elec Engng

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“…In the past decade, plenty of watermarking schemes [1,2] have been proposed, which can be classified into several categories. According to the embedding rules, the watermarking can be classified into the additive watermarking [3][4][5][6], the multiplicative watermarking [7,8], the quantization watermarking [9], and so on. In this paper, the additive watermarking will be focused on.…”

Section: Introductionmentioning

confidence: 99%

“…In [3], the cloud watermark generated by the cloud model is used to protect the digital content by additive rule. In [4], a series of additive watermark detectors are proposed according to the hierarchical, two-level spatial image model, which include a generalized likelihood ratio, the Bayesian, and the Rao test detectors. In [5], the watermark is embedded into the coefficients of the dual-tree complex wavelet transform using a simple human visual model, and simultaneously, the corresponding detector is proposed In the general Internet environment, the multiwatermarking can be used to identify the ownership of the multimedia content to guarantee the legal redistribution of multimedia content.…”

Section: Introductionmentioning

confidence: 99%

On multiwatermarking in cloud environment

Wang

Lian

2011

Concurrency and Computation

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SUMMARY Multiwatermarking is the technique to embed multiple messages into the same media content. It can be used to identify media content's owners or trace the illegal redistributors in media distribution. Especially in cloud environment with multiple content providers or users, multiwatermarking may have potential applications. However, some performances of multiple watermark embedding, for example, the distortion and security, are not considered. In this paper, the application scenarios of multiwatermarking in cloud environment are figured out, which investigate the secure media distribution models suitable for cloud‐based platform. Additionally, the multiwatermarking based on additive rule is studied and then analyzed theoretically from two aspects, one of which is the multi‐embedding of same watermark sequence and the other is the multiembedding of different watermark sequences. The analysis emphasizes on four aspects; that is, robustness, capacity, distortion, and security. And each of which considers two cases, that is, the authorized embedding and unauthorized embedding. Additionally, the correlation detection based on multiwatermarking is proposed, and for each case, the corresponding suggestions to improve system performance are proposed. Some comparative experiments are performed, and results are given to show the validity of these theoretical analysis.Copyright © 2011 John Wiley & Sons, Ltd.

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“…The watermark is detected at the receiver side without the original media content which, in this blind case, is treated as a noise source. The additive SS [6] watermarking spreads the watermark over the host signal uniformly while in multiplicative SS [7,8], the watermark spreads according to the unreeling content of the host signal. To decrease the noise effect of the host signal in additive SS, Malvar and Florencio [9] proposed an improved spread spectrum (ISS) -a new modulation scheme which exploits the side information at the encoder to reduce the effect of host signal and increase the decoding performance [10].…”

Section: Introductionmentioning

confidence: 99%

Information hiding techniques for infrared images: exploring the state-of-the art and challenges

et al. 2015

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The proliferation of Infrared technology and imaging systems enables a different perspective to tackle many computer vision problems in defense and security applications. Infrared images are widely used by the law enforcement, Homeland Security and military organizations to achieve a significant advantage or situational awareness, and thus is vital to protect these data against malicious attacks. Concurrently, sophisticated malware are developed which are able to disrupt the security and integrity of these digital media. For instance, illegal distribution and manipulation are possible malicious attacks to the digital objects. In this paper we explore the use of a new layer of defense for the integrity of the infrared images through the aid of information hiding techniques such as watermarking.In this context, we analyze the efficiency of several optimal decoding schemes for the watermark inserted into the Singular Value Decomposition (SVD) domain of the IR images using an additive spread spectrum (SS) embedding framework. In order to use the singular values (SVs) of the IR images with the SS embedding we adopt several restrictions that ensure that the values of the SVs will maintain their statistics. For both the optimal maximum likelihood decoder and sub-optimal decoders we assume that the PDF of SVs can be modeled by the Weibull distribution. Furthermore, we investigate the challenges involved in protecting and assuring the integrity of IR images such as data complexity and the error probability behavior, i.e., the probability of detection and the probability of false detection, for the applied optimal decoders. By taking into account the efficiency and the necessary auxiliary information for decoding the watermark, we discuss the suitable decoder for various operating situations. Experimental results are carried out on a large dataset of IR images to show the imperceptibility and efficiency of the proposed scheme against various attack scenarios.

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Additive Watermark Detectors Based on a New Hierarchical Spatially Adaptive Image Model

Cited by 8 publications

References 32 publications

Optimum watermark detection of ultrasonic echo medical images

Optimum watermark detection of ultrasonic echo medical images

On multiwatermarking in cloud environment

Information hiding techniques for infrared images: exploring the state-of-the art and challenges

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