A robust watermarking method based on Compressed Sensing and Arnold scrambling

Veena, V. K.; Lal, G. Jyothish; Prabhu, S.; Kumar, Sanjeev; Soman, K P

doi:10.1109/mvip.2012.6428771

Cited by 21 publications

(5 citation statements)

References 13 publications

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“…Moreover, scrambling technology is reversible, and a scrambled image can be recovered completely lossless. In scrambling technology, Arnold transformation has been widely studied and the theoretical system is relatively complete [26][27][28]. Therefore, we have elected to adopt Arnold transformation in our method to encrypt images.…”

Section: Arnold Transformationmentioning

confidence: 99%

A partial encryption algorithm for medical images based on quick response code and reversible data hiding technology

Zhang

et al. 2020

BMC Med Inform Decis Mak

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Background Medical image data, like most patient information, have a strong requirement for privacy and confidentiality. This makes transmitting medical image data, within an open network, problematic, due to the aforementioned issues, along with the dangers of data/information leakage. Possible solutions in the past have included the utilization of information-hiding and image-encryption technologies; however, these methods can cause difficulties when attempting to recover the original images. Methods In this work, we developed an algorithm for protecting medical image key regions. Coefficient of variation is first employed to identify key regions, a.k.a. image lesion areas; then additional areas are processed as blocks and texture complexity is analyzed. Next, our novel reversible data-hiding algorithm embeds lesion area contents into a high-texture area, after which an Arnold transformation is utilized to protect the original lesion information. After this, we use image basic information ciphertext and decryption parameters to generate a quick response (QR) code used in place of original key regions. Results The approach presented here allows for the storage (and sending) of medical image data within open network environments, while ensuring only authorized personnel are able to recover sensitive patient information (both image and meta-data) without information loss. Discussion Peak signal to noise ratio and the Structural Similarity Index measures show that the algorithm presented in this work can encrypt and restore original images without information loss. Moreover, by adjusting the threshold and the Mean Squared Error, we can control the overall quality of the image: the higher the threshold, the better the quality and vice versa. This allows the encryptor to control the amount of degradation as, at appropriate amounts, degradation aids in the protection of the image. Conclusions As shown in the experimental results, the proposed method allows for (a) the safe transmission and storage of medical image data, (b) the full recovery (no information loss) of sensitive regions within the medical image following encryption, and (c) meta-data about the patient and image to be stored within and recovered from the public image.

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Section: Arnold Transformationmentioning

confidence: 99%

A partial encryption algorithm for medical images based on quick response code and reversible data hiding technology

Zhang

et al. 2020

BMC Med Inform Decis Mak

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“…However, due to the distortion during the process, the receiver cannot extract watermark exactly. In [31], CS is used to process watermarked image, which ensures the security of both watermark and cover image. However, the accuracy of watermark extraction is at risk.…”

Section: Related Workmentioning

confidence: 99%

A Novel Image Authentication with Tamper Localization and Self-Recovery in Encrypted Domain Based on Compressive Sensing

Zhang

Xiao

Chang

2018

Security and Communication Networks

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This paper proposes a novel tamper detection, localization, and recovery scheme for encrypted images with Discrete Wavelet Transformation (DWT) and Compressive Sensing (CS). The original image is first transformed into DWT domain and divided into important part, that is, low-frequency part, and unimportant part, that is, high-frequency part. For low-frequency part contains the main information of image, traditional chaotic encryption is employed. Then, high-frequency part is encrypted with CS to vacate space for watermark. The scheme takes the processed original image content as watermark, from which the characteristic digest values are generated. Comparing with the existing image authentication algorithms, the proposed scheme can realize not only tamper detection and localization but also tamper recovery. Moreover, tamper recovery is based on block division and the recovery accuracy varies with the contents that are possibly tampered. If either the watermark or low-frequency part is tampered, the recovery accuracy is 100%. The experimental results show that the scheme can not only distinguish the type of tamper and find the tampered blocks but also recover the main information of the original image. With great robustness and security, the scheme can adequately meet the need of secure image transmission under unreliable conditions.

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“…Here data hiding, compression and security is taken into account. Transform domain watermarking brings better result when compared to spatial domain watermarking in terms of visual quality [3]. In spatial domain watermarking, since only fourbit planes are embedded, the sizes of message image and host image are the same.…”

Section: Viconclusionmentioning

confidence: 99%

Transform Domain Watermarking Based on Compressive Sensing and Scrambling

Thomas¹

2015

IJAREEIE

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ABSTRACT:Conventional sampling follows Nyquist Criterion which states that effective reconstruction of a signal is possibleonly if the signal is sampled at a rate greater than or equal to twice its largest frequency. Compressive Sensing is a new method by which a signal can be reconstructed with fewer samples. Watermarking is the process of hiding data in a carrier signal. Watermarking has been combined with compressive sensing in this paper. To enhance the security of the watermarked image, it is scrambled by random distribution of pixels. PSNR of the watermarked image and the recovered image is compared with respect to embedding ratio. Transform domain is done in this paper as a modification to spatial domain watermarking. Entire image is embedded in transform domain watermarking rather than just four planes which results in the better result.

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A robust watermarking method based on Compressed Sensing and Arnold scrambling

Cited by 21 publications

References 13 publications

A partial encryption algorithm for medical images based on quick response code and reversible data hiding technology

A partial encryption algorithm for medical images based on quick response code and reversible data hiding technology

A Novel Image Authentication with Tamper Localization and Self-Recovery in Encrypted Domain Based on Compressive Sensing

Transform Domain Watermarking Based on Compressive Sensing and Scrambling

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