Wavelet-based watermarking of remotely sensed imagery tailored to classification performance

Ziegeler, Sean; Tamhankar, H.; Fowler, James E.; Bruce, Lori

doi:10.1109/warsd.2003.1295202

Cited by 12 publications

(8 citation statements)

References 3 publications

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“…There are different means of achieving this objective. The literature reports that this has been done in three different ways: i) using a more sophisticated transform, such as the RDWT [9]; ii) embedding the watermark in a suitable subset of subbands of the wavelet transformed image [10]; and iii) tuning the scaling parameter [13]. In this paper, we propose a simple and effective user-controlled alternative that shows interesting properties as will be discussed in the next section.…”

Section: Proposed Mfssw Methodsmentioning

confidence: 99%

“…The choice of the RDWT is motivated by its capability to accommodate a greater amount of information added to data for a given distortion level with respect to the traditional DWT. In [10], the random watermark is distributed over the different levels of the DWT decomposition by selecting, for each level, the subband that maximizes the root-mean-square error criterion (i.e., the subband with highest impact on image quality). In [11], the original multispectral image is transformed by means of a 3D DWT, such that the size of the highest level of the transformed image fits the one of the wavelet transformed image watermark.…”

Section: Previous Workmentioning

confidence: 99%

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Near-lossless spread spectrum watermarking for multispectral remote sensing images

Melgani

Benzid

Natale

2007

J. Appl. Remote Sens

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Watermarking represents a potentially effective tool for the protection and verification of ownership rights in remotely sensed imagery. Such data however cannot undergo critical quality degradation processes, for they must ensure accurate extraction of thematic and scientific information. In this paper, we propose to adapt a state-of-the-art spread spectrum method found to be particularly powerful for real world images to make the watermark insertion process near-lossless while preserving most of its effective robustness capability. This is done by embedding the watermark in the middle-frequency range of the discrete cosine transform (DCT) domain instead of the low-frequency range. The exact position is determined by a numerical root-finding method, targeted to achieve a userspecified minimum level of robustness against a given ensemble of attacks. In this way, an optimal trade-off can be obtained between quality and robustness. Thorough experimental tests over a multispectral remote sensing image show that the proposed method allows to trade a contained loss in robustness with a significant gain in image quality, quantified in terms of both peak signal-to-noise ratio (PSNR) and impact on classification performance. In addition, they reveal an interesting property of the watermark insertion shifting into the middlefrequency range, consisting of a sharp increase in robustness to the widely used cropping operation, which is considered the most critical attack in remote sensing imagery.

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Section: Proposed Mfssw Methodsmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Near-lossless spread spectrum watermarking for multispectral remote sensing images

Melgani

Benzid

Natale

2007

J. Appl. Remote Sens

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“…Entropy E for an image I is given by (6). Here, P r (α i ) is the occurrence probability of the gray level α i and G is the total number of gray levels.…”

Section: Error Analysis Measuresmentioning

confidence: 99%

“…These aspects can be very well handled by technologies like watermarking and encryption. There is plenty of literature available for watermarking of multispectral images both in spatial and frequency domain [1]- [6]. However, none of them have considered security aspect of watermarked multispectral images at dissemination.…”

Section: Introductionmentioning

confidence: 99%

Secure Dissemination and Protection of Multispectral Images Using Crypto-Watermarking

Chaudhari

Venkatachalam

Buddhiraju

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Copyright protection of multispectral images and its integrity become one of the key problems in spatial information service due to rapid development of communication network. This paper depicts a crypto-watermarking scheme by combining watermarking and encryption to protect copyright of multispectral images and to provide security to the watermarked image at dissemination level. We have proposed wavelet-based watermarking and multiplicative-transposition-based cipher for encryption. Experimental results show that the proposed algorithm has not only good robustness against encryption attacks, but also has transparency, strongness, large data hiding capacity, correct extraction of watermark, and strong robustness against JPEG lossy compression, filtering, and noise. Moreover, the classification of original and watermarked data is performed and it is observed that the proposed watermarking scheme leads to less misclassification and it meets all the requirements for copyright protection as well as the security of multispectral images in an effective manner.

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“…The size of the watermark has been adjusted to test the intensity and robustness of watermark when an unsupervised classifier was performed on the satellite image. Ziegeler et al (2003) have illustrated that the digital watermarking techniques developed for multimedia data cannot be directly applied to the satellite images due to the fact that the analytic integrity of the data, rather than perceptual quality, is of primary importance. Thus a DWT-based algorithm for the watermarking of remotely sensed images was proposed.…”

Section: Review Of Related Researchesmentioning

confidence: 99%

Feature-Based Digital Watermarking for Remote Sensing Images

Hsu

Chen

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:With the rapid development of information and communication technology, people can acquire and distribute many kinds of digital data more conveniently than before. The consequence is that the "copyright protection" which prevents digital data from been duplicated illegally should be paid much more attention. Digital watermarking is the process of embedding visible or invisible information into a digital signal which may be used to verify its authenticity or the identity of its owners. In the past, digital watermarking technology has been successfully applied to the "copyright protection" of multimedia data, however the researches and applications of applying digital watermarking to geo-information data are still very inadequate. In this study, a novel digital watermarking algorithm based on the scale-space feature points is applied to the remote sensing images, and the robustness of the embedded digital watermark and the impact on satellite image quality are evaluated and analysed. This kind of feature points are commonly invariant to Image rotation, scaling and translation, therefore they naturally fit into the requirement of geometrically robust image watermarking. The experiment results show almost all extracted watermarks have high values of normal correlation and can be recognized clearly after the processing of image compression, brightness adjustment and contrast adjustment. In addition, most of the extracted watermarks are identified after the geometric attacks. Furthermore, the unsupervised image classification is implemented on the watermarked images to evaluate the image quality reduction and the results show that classification accuracy is affected slightly after embedding watermarks into the satellite images.

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Wavelet-based watermarking of remotely sensed imagery tailored to classification performance

Cited by 12 publications

References 3 publications

Near-lossless spread spectrum watermarking for multispectral remote sensing images

Near-lossless spread spectrum watermarking for multispectral remote sensing images

Secure Dissemination and Protection of Multispectral Images Using Crypto-Watermarking

Feature-Based Digital Watermarking for Remote Sensing Images

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