Joint Crypto-Stego Scheme for Enhanced Image Protection With Nearest-Centroid Clustering

Shifa, Amna; Afgan, Muhammad S.; Asghar, Mamoona Naveed; Fleury, Martin; Memon, Imran; Abdullah, Saima; Rasheed, Nadia

doi:10.1109/access.2018.2815037

Cited by 22 publications

(8 citation statements)

References 30 publications

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“…In fact, that computational overhead motivated the development of the proposed technique to modify LSB steganography. Similarly, LSB-M [18] first identifies the intensity clusters in the image and then hides data inside those clusters, again a time-complex process, which is not suitable for video steganography. Other techniques including a DWT-Haar wavelet-based method [13], hash-LSB [17] and secret key LSB [14] suffer the same problems of not being able to run fast enough to match the video speed.…”

Section: Resultsmentioning

confidence: 99%

“…This spatial approach ensures that the overall visual aesthetic of the image is not significantly altered. Initial research into LSB steganography concentrated on designing the system to increase the payload capacity by utilizing most of the cover-image pixels [10]- [18]. However, steganalysis techniques soon became strong enough to break such systems using statistical analysis, which was developed to identify the regular patterns by which data were stored inside the cover [19]- [21].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, there was a need for robust LSB techniques based on cryptography-steganography, which can evade such steganalysis attacks. The subsequent research into the Stego Color Cycle (SCC) approach [22], Magic LSB technique [16], hash-LSB [17] and nearest-centroid clustering (LSB-M) [18] are comparatively recent such additions to the field of steganography. In this article, as described in detail in Section III, after per video frame identification of a starting pixel or seed, random pixel selection for embedding the data within LSBs takes place within the neighborhood of the seed.…”

Section: Introductionmentioning

confidence: 99%

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Preserving Chain-of-Evidence in Surveillance Videos for Authentication and Trust-Enabled Sharing

et al. 2020

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Surveillance video recording is a powerful method of deterring unlawful activities. A robust data protection-by-design solution can be helpful in terms of making a captured video immutable, as such recordings cannot become a piece of evidence until proven to be unaltered. Similarly, video sharing from closed-circuit television video recording or in social media interaction requires self-authentication for responsible and reliable data sharing. This article presents a computationally inexpensive method of preserving a chain-of-evidence in surveillance videos by means of hashing and steganography. The method conforms to the data protection regulations, which are increasingly adopted by governments, and is applicable to network edge storage. Encryption keys are stored in a hardware wallet independently of the video capture device itself, while evidential information is stored steganographically within video frames themselves, independently of the content. Added protection is provided by hiding information within the two least-valued of pixel bitplanes, using a newly introduced technique that randomizes the pixel storage locations on a per video frame and video-capture device basis. Overall, the proposed method has turned out to not only preserve the integrity of stored video data but also results in minimal degradation of the video data resulting from steganography. Despite the inclusion of hidden information, video frames will still be available for common image-processing tasks such as tracking and classification, as their objective video quality is almost unchanged.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Preserving Chain-of-Evidence in Surveillance Videos for Authentication and Trust-Enabled Sharing

et al. 2020

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“…Those video syntax elements, encrypted as part of a selective encryption (SE) scheme, also preserve decoder format compliance, in the sense that the encrypted video can still be processed even if the selected syntax elements are not decrypted [ 9 ]. Notice also that if the authors’ contribution in [ 10 ] is applied, keys may alternatively be embedded within the encrypted video itself through the joint application of steganography and cryptography, though that aspect is outside the scope of the current paper.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Cipher for H.264 Videos in the Internet of Multimedia Things with Encryption Space Ratio Diagnostics

Shifa

Asghar

Noor

et al. 2019

Sensors

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Within an Internet of Multimedia Things, the risk of disclosing streamed video content, such as that arising from video surveillance, is of heightened concern. This leads to the encryption of that content. To reduce the overhead and the lack of flexibility arising from full encryption of the content, a good number of selective-encryption algorithms have been proposed in the last decade. Some of them have limitations, in terms of: significant delay due to computational cost, or excess memory utilization, or, despite being energy efficient, not providing a satisfactory level of confidentiality, due to their simplicity. To address such limitations, this paper presents a lightweight selective encryption scheme, in which encoder syntax elements are encrypted with the innovative EXPer (extended permutation with exclusive OR). The selected syntax elements are taken from the final stage of video encoding that is during the entropy coding stage. As a diagnostic tool, the Encryption Space Ratio measures encoding complexity of the video relative to the level of encryption so as to judge the success of the encryption process, according to entropy coder. A detailed comparative analysis of EXPer with other state-of-the-art encryption algorithms confirms that EXPer provides significant confidentiality with a small computational cost and a negligible encryption bitrate overhead. Thus, the results demonstrate that the proposed security scheme is a suitable choice for constrained devices in an Internet of Multimedia Things environment.

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“…Since roads are a principal part of modern transportation, managing and updating road information in the Geographic Information System database is of great concern [1]. Road information is fundamental geographical information playing an important role in many applications, e.g., serving as reference for the recognition of other objects and travel recommendation [2], road navigation, geometric correction of images, and even assisting confidential transmission of color images [3]. Automatic road extraction is an effective and economic way to obtain road information.…”

Section: Introductionmentioning

confidence: 99%

Road Extraction from VHR Remote-Sensing Imagery via Object Segmentation Constrained by Gabor Features

Chen

Zhu

Xie

et al. 2018

IJGI

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Automatic road extraction from remote-sensing imagery plays an important role in many applications. However, accurate and efficient extraction from very high-resolution (VHR) images remains difficult because of, for example, increased data size and superfluous details, the spatial and spectral diversity of road targets, disturbances (e.g., vehicles, shadows of trees, and buildings), the necessity of finding weak road edges while avoiding noise, and the fast-acquisition requirement of road information for crisis response. To solve these difficulties, a two-stage method combining edge information and region characteristics is presented. In the first stage, convolutions are executed by applying Gabor wavelets in the best scale to detect Gabor features with location and orientation information. The features are then merged into one response map for connection analysis. In the second stage, highly complete, connected Gabor features are used as edge constraints to facilitate stable object segmentation and limit region growing. Finally, segmented objects are evaluated by some fundamental shape features to eliminate nonroad objects. The results indicate the validity and superiority of the proposed method to efficiently extract accurate road targets from VHR remote-sensing images.

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Joint Crypto-Stego Scheme for Enhanced Image Protection With Nearest-Centroid Clustering

Cited by 22 publications

References 30 publications

Preserving Chain-of-Evidence in Surveillance Videos for Authentication and Trust-Enabled Sharing

Preserving Chain-of-Evidence in Surveillance Videos for Authentication and Trust-Enabled Sharing

Lightweight Cipher for H.264 Videos in the Internet of Multimedia Things with Encryption Space Ratio Diagnostics

Road Extraction from VHR Remote-Sensing Imagery via Object Segmentation Constrained by Gabor Features

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