One-pass lossless data hiding and compression of remote sensing data

Carpentieri, Bruno; Castiglione, Arcangelo; Santis, Alfredo De; Palmieri, Francesco; Pizzolante, Raffaele

doi:10.1016/j.future.2018.07.051

Cited by 36 publications

(20 citation statements)

References 34 publications

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“…To date, many RDH methods that work with plaintext images have been proposed. The basic ideas of these methods are different expansion (DE) [5,6], histogram shift (HS) [7,8], pixel value ordering (PVO) [9,10] and the modification of prediction errors [11,12]. All of these methods use the spatial correlation and the redundancy of plaintext images to embed additional bits.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Error-Free Reversible Data Hiding in Encrypted Images Using (7, 4) Hamming Code and Most Significant Bit Prediction

Chen

Chang

2019

Symmetry

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In this paper, a novel, real-time, error-free, reversible data hiding method for encrypted images has been proposed. Based on the (7, 4) Hamming code, we designed an efficient encoding scheme to embed secret data into the least significant bits (LSBs) of the encrypted image. For reversibility, we designed a most significant bit (MSB) prediction scheme that can recover a portion of the modified MSBs after the image is decrypted. These MSBs can be modified to accommodate the additional information that is used to recover the LSBs. After embedding the data, the original image can be recovered with no error and the secret data can be extracted from both the encrypted image and the decrypted image. The experimental results proved that compared with existing methods, the proposed method can achieve higher embedding rate, better quality of the marked image and less execution time of data embedding. Therefore, the proposed method is suitable for real-time applications in the cloud.

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Section: Introductionmentioning

confidence: 99%

Real-Time Error-Free Reversible Data Hiding in Encrypted Images Using (7, 4) Hamming Code and Most Significant Bit Prediction

Chen

Chang

2019

Symmetry

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“…From Table 4 , the following observations can be made with respect to the selected properties: All the schemes are applied to the remote sensing images. In some schemes [ 11 , 14 ], multispectral images are used, while in other systems [ 10 , 13 , 16 ] including the proposed scheme, hyperspectral images are considered. The scheme proposed by [ 12 ] is proposed for raster images, but can be extended to hyperspectral images.…”

Section: Resultsmentioning

confidence: 99%

“…Reference [ 16 ] presents a novel one-pass framework based on a modification of prediction errors scheme, which enables data hiding and lossless compression operations simultaneously on hyperspectral images collected through remote sensing facilities. The scheme employs three-dimensional predictors to exploit the redundancies of hyperspectral images.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Semi-Fragile Watermarking of Remote Sensing Images in the Wavelet Domain

Serra-Ruiz

Qureshi

Megías

2019

Entropy

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This article presents a semi-fragile image tampering detection method for multi-band images. In the proposed scheme, a mark is embedded into remote sensing images, which have multiple frequential values for each pixel, applying tree-structured vector quantization. The mark is not embedded into each frequency band separately, but all the spectral values (known as signature) are used. The mark is embedded in the signature as a means to detect if the original image has been forged. The image is partitioned into three-dimensional blocks with varying sizes. The size of these blocks and the embedded mark is determined by the entropy of each region. The image blocks contain areas that have similar pixel values and represent smooth regions in multispectral or hyperspectral images. Each block is first transformed using the discrete wavelet transform. Then, a tree-structured vector quantizer (TSVQ) is constructed from the low-frequency region of each block. An iterative algorithm is applied to the generated trees until the resulting tree fulfils a requisite criterion. More precisely, the TSVQ tree that matches a particular value of entropy and provides a near-optimal value according to Shannon's rate-distortion function is selected. The proposed method is shown to be able to preserve the embedded mark under lossy compression (above a given threshold) but, at the same time, it detects possibly forged blocks and their positions in the whole image. Experimental results show how the scheme can be applied to detect forgery attacks, and JPEG2000 compression of the images can be applied without removing the authentication mark. The scheme is also compared to other works in the literature.

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“…Many motivations lead us to the use of steganography and they can be significantly different from each other. For example, there may be the need to establish a “covert channel,” as well as to insert a seal into a digital content, which cannot be removed or altered (ie, digital watermarking). It is important to point out that unlike cryptography, which deals with the protection of the message content, steganography deals with techniques to hide the presence of hidden information within a message or within stored/exchanged data.…”

Section: Introductionmentioning

confidence: 99%

Compression‐based steganography

Carpentieri

Castiglione

Santis

et al. 2019

Concurrency and Computation

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Summary Conventional privacy‐enforcement mechanisms, such as encryption‐based ones, are frequently used to prevent third‐party eavesdroppers to intercept confidential information exchanged between two or more parties. However, the use of such mechanisms can be perceivable and it alerts the involved intercepting entities that could devote some effort in trying to remove the protection, eg, by cracking the encryption keys used or by exploiting the vulnerabilities of the technological solution used to protect the data. Sometimes, from the security point of view, avoiding to draw the attention or suspect to intermediate intercepting entities, may be better than protecting a data in a conventional manner. In such direction, one of the most effective approaches is hiding the secret information to be exchanged inside other data, through steganographic techniques. In this work, we exploit, for this specific purpose, the hierarchical structure of a compressed archive, as well as the algorithms and parameters used to create and maintain such archive. It is important to point out that, by doing this, the secret information is in no way semantically related to the contents of the compressed archive. This can be extremely useful in many cloud‐based situations where several confidential data is moved across multiple independent data center, which are under the control of different and not always fully trusted authorities. The effectiveness of this proposal has been assessed by using a properly designed and implemented prototype, where extensive tests have been performed within the context of a proof‐of‐concept.

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One-pass lossless data hiding and compression of remote sensing data

Cited by 36 publications

References 34 publications

Real-Time Error-Free Reversible Data Hiding in Encrypted Images Using (7, 4) Hamming Code and Most Significant Bit Prediction

Real-Time Error-Free Reversible Data Hiding in Encrypted Images Using (7, 4) Hamming Code and Most Significant Bit Prediction

Entropy-Based Semi-Fragile Watermarking of Remote Sensing Images in the Wavelet Domain

Compression‐based steganography

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