Hiding data in compressive sensed measurements: A conditionally reversible data hiding scheme for compressively sensed measurements

Yamaç, Mehmet; Dikici, Çağatay; Sankur, B.

doi:10.1016/j.dsp.2015.09.017

Cited by 14 publications

(14 citation statements)

References 38 publications

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“…Compressive sensing measurements: In contrast to recent data hiding techniques that adopt compressive sensing (CS) for improving the security of their solutions, Yamac et al [95] proposed a data hiding method for CS measurements in which the host signal (x) is represented by far few measurements (y) than conventional methods . The key premise in the CS is to reconstruct the N-dimensional vector x, from the m-sparse vector y, i.e.…”

Section: G Othersmentioning

confidence: 99%

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On the Properties of Non-Media Digital Watermarking: A Review of State of the Art Techniques

et al. 2016

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Section: G Othersmentioning

confidence: 99%

“…wm system Data sparsity level limits embedding capacity and reliable wm detection a general framework for hiding metadata into CS measurements such that conceal information coexists with the host data only in the CS form[95] …”

mentioning

confidence: 99%

On the Properties of Non-Media Digital Watermarking: A Review of State of the Art Techniques

et al. 2016

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“…However, their application to reversible de-identification schemes is not straightforward since the corruption induced during the de-identification stage needs to be separately transmitted in a side channel. To this effect, the work in [21], [22] introduces such a steganographic channel that enables embedding some extra information directly on compressively sensed measurements. This steganographic channel and the resulting data hiding can allow efficient transmission of the de-identification information.…”

Section: B One-class and Multi-class Encryption Via Compressive Sensingmentioning

confidence: 99%

“…The recovery algorithm for the fully authorized user is provided in Algorithm 2. The recovery guarantee conditions, as well as a robustness analysis of the proposed embedding and recovery algorithm are provided in [21], [23]. Random, e.g., Gaussian measurement matrices are proven to be optimal for reconstruction performances.…”

Section: B Recovery Algorithms For Different Type Of Usersmentioning

confidence: 99%

Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing

Yamaç

Ahishali

Passalis

et al. 2019

2019 27th European Signal Processing Conference (EUSIPCO)

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Security monitoring via ubiquitous cameras and their more extended in intelligent buildings stand to gain from advances in signal processing and machine learning. While these innovative and ground-breaking applications can be considered as a boon, at the same time they raise significant privacy concerns. In fact, recent GDPR (General Data Protection Regulation) legislation has highlighted and become an incentive for privacypreserving solutions. Typical privacy-preserving video monitoring schemes address these concerns by either anonymizing the sensitive data. However, these approaches suffer from some limitations, since they are usually non-reversible, do not provide multiple levels of decryption and computationally costly. In this paper, we provide a novel privacy-preserving method, which is reversible, supports de-identification at multiple privacy levels, and can efficiently perform data acquisition, encryption and data hiding by combining multi-level encryption with compressive sensing. The effectiveness of the proposed approach in protecting the identity of the users has been validated using the goodness of reconstruction quality and strong anonymization of the faces.

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“…In the schemes of data hiding in CS domain, both the cover data (sparse samples) and the embedded data are exactly recovered under certain noise, payload and sparsity conditions, so these methods can be qualified as conditionally reversible data hiding [Yamaç , Dikici and Sankur (2016)]. In our scheme, CS is the only part of the whole process that will bring the loss, and the sensing object of CS is the prediction error.…”

Section: Evaluation Of the Proposed Schemementioning

confidence: 99%

High Capacity Data Hiding in Encrypted Image Based on Compressive Sensing for Nonequivalent Resources

Xiao

Liang

Qing

et al. 2019

Computers, Materials &Amp; Continua

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To fulfill the requirements of data security in environments with nonequivalent resources, a high capacity data hiding scheme in encrypted image based on compressive sensing (CS) is proposed by fully utilizing the adaptability of CS to nonequivalent resources. The original image is divided into two parts: one part is encrypted with traditional stream cipher; the other part is turned to the prediction error and then encrypted based on CS to vacate room simultaneously. The collected non-image data is firstly encrypted with simple stream cipher. For data security management, the encrypted non-image data is then embedded into the encrypted image, and the scrambling operation is used to further improve security. Finally, the original image and non-image data can be separably recovered and extracted according to the request from the valid users with different access rights. Experimental results demonstrate that the proposed scheme outperforms other data hiding methods based on CS, and is more suitable for nonequivalent resources. encryption together through matrix multiplication [Rachlin and Baron (2008)]. Compared with the stream cipher encryption, the encrypted data based on CS can reduce bandwidth resources effectively. Due to this feature of CS, research results based on CS often serve multimedia data compression and representation [Wu, Yu, Yuan et al. (2016)]. Meanwhile, CS has a low computation cost in the sensing part while the computation of recovery is rather complex at the receiving end. Information hiding plays an important role in protecting various information from being destroyed [Cao, Zhou, Sun et al. (2018)]. Within the current application of CS in reversible data hiding, there are two main types: the first one usually embeds data in the samples of DCT/DWT and then uses CS to compress [Xiao and Chen (2014)]. The other one embeds data in the measured value [Cao, Du, Wei et al. (2016); Li, Xiao and Zhang (2016); Pan, Li, Yang et al. (2015)]. However, both of the two schemes have some defects. The first one is more suitable for digital watermarking rather than data hiding, which focuses on the protection of the copyright about the carrier and the robustness of the watermark. But, data hiding puts more emphasis on the capacity and security of the embedded information itself. The scheme proposed by Pan et al. [Pan, Li, Yang et al. (2015)] is a watermarking scheme for plain image only which does not provide security for the cover. The scheme proposed by Cao et al. [Cao, Du, Wei et al. (2016)] is not suitable for nonequivalent resources, although this scheme has a brilliant performance on recovery. The reason is that sparse representation to vacate room for data embedding in the preprocessing operation is very complicated. The scheme proposed by Li et al. [Li, Xiao and Zhang (2016)] is a smart data hiding scheme based on block compressive

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Hiding data in compressive sensed measurements: A conditionally reversible data hiding scheme for compressively sensed measurements

Cited by 14 publications

References 38 publications

On the Properties of Non-Media Digital Watermarking: A Review of State of the Art Techniques

On the Properties of Non-Media Digital Watermarking: A Review of State of the Art Techniques

Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing

High Capacity Data Hiding in Encrypted Image Based on Compressive Sensing for Nonequivalent Resources

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