Separable Reversible Data Hiding in Encrypted Images With High Capacity Based on Median-Edge Detector Prediction

Liu, Li; Wang, Anhong; Chang, Chin‐Chen

doi:10.1109/access.2020.2972736

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…A possible strategy is to make compromises on security by utilising bespoke encryption schemes in exchange for the redundancy and compressibility of encrypted objects [28][29][30][31][32]. Another strategy is to preprocess the cover objects prior to encryption in order to create space for the subsequent data embedding in the cryptospace [33][34][35][36][37][38][39]. From our perspective, both strategies have limitations and their practicality might be open to dispute.…”

Section: Introductionmentioning

confidence: 99%

Cryptospace Invertible Steganography with Conditional Generative Adversarial Networks

Chang

2021

Security and Communication Networks

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Deep neural networks have become the foundation of many modern intelligent systems. Recently, the author has explored adversarial learning for invertible steganography (ALIS) and demonstrated the potential of deep neural networks to reinvigorate an obsolete invertible steganographic method. With the worldwide popularisation of the Internet of things and cloud computing, invertible steganography can be recognised as a favourable way of facilitating data management and authentication due to the ability to embed information without causing permanent distortion. In light of growing concerns over cybersecurity, it is important to take a step forwards to investigate invertible steganography for encrypted data. Indeed, the multidisciplinary research in invertible steganography and cryptospace computing has received considerable attention. In this paper, we extend previous work and address the problem of cryptospace invertible steganography with deep neural networks. Specifically, we revisit a seminal work on cryptospace invertible steganography in which the problem of message decoding and image recovery is viewed as a type of binary classification. We formulate a general expression encompassing spatial, spectral, and structural analyses towards this particular classification problem and propose a novel discrimination function based on a recurrent conditional generative adversarial network (RCGAN) which predicts bit-planes with stacked neural networks in a top-down manner. Experimental results evaluate the performance of various discrimination functions and validate the superiority of neural-network-aided discrimination function in terms of classification accuracy.

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Section: Introductionmentioning

confidence: 99%

Cryptospace Invertible Steganography with Conditional Generative Adversarial Networks

Chang

2021

Security and Communication Networks

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“…To do this, a location map is generated, which is compressed using arithmetic coding and embedded in the cover image along with the encrypted message. A similar method is presented in [65]. It is based on a median-edge detector, which predicts image pixels.…”

Section: A Reversible Embeddingmentioning

confidence: 99%

Digital Steganography and Watermarking for Digital Images: A Review of Current Research Directions

2020

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The development of information technology has led to a significant increase in the share of multimedia traffic in data networks. This has necessitated to solve the following information security tasks in relation to multimedia data: protection against leakage of confidential information, as well as identifying the source of the leak; ensuring the impossibility of unauthorized changes; copyright protection for digital objects. To solve such kind of problems, methods of steganography and watermarking are designed that implement embedding in digital objects hidden information sequences for various purposes. In this paper, an overview of promising research in the specified area is provided. First of all, we provide basic information about this field of research and consider the main applications of its methods. Next, we review works demonstrating current trends in the development of methods and algorithms for data hiding in digital images. This review is not exhaustive; it focuses on contemporary works illustrating current research directions in the field of information embedding in digital images. This is the main feature of review, which distinguishes it from previously published reviews. The paper concludes with an analysis of identified problems in the field of digital steganography and digital watermarking.

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“…Data hiding methods can also be divided into two kinds: reversible and irreversible, depending on the receiver’s ability to recover the cover digital image. Reversible data hiding (RDH) [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ] can recover the primary digital image after reading the embedded secret messages. While irreversible data hiding (IRDH) [ 14 , 15 ] cannot recover the primary digital image from the message-embedded confidential image completely without loss.…”

Section: Introductionmentioning

confidence: 99%

Embedding Biometric Information in Interpolated Medical Images with a Reversible and Adaptive Strategy

Chi

Horng

Chang

et al. 2022

Sensors

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How to hide messages in digital images so that messages cannot be discovered and tampered with is a compelling topic in the research area of cybersecurity. The interpolation-based reversible data hiding (RDH) scheme is especially useful for the application of medical image management. The biometric information of patients acquired by biosensors is embedded into an interpolated medical image for the purpose of authentication. The proposed scheme classifies pixel blocks into complex and smooth ones according to each block’s dynamic range of pixel values. For a complex block, the minimum-neighbor (MN) interpolation followed by DIM embedding is applied, where DIM denotes the difference between the block’s interpolated pixel values and the maximum pixel values. For a smooth block, the block mean (BM) interpolation is followed by a prediction error histogram (PEH) embedding and a difference expansion (DE) embedding is applied. Compared with previous methods, this adaptive strategy ensures low distortion due to embedding for smooth blocks while it provides a good payload for complex blocks. Our scheme is suitable for both medical and general images. Experimental results confirm the effectiveness of the proposed scheme. Performance comparisons with state-of-the-art schemes are also given. The peak signal to noise ratio (PSNR) of the proposed scheme is 10.32 dB higher than the relevant works in the best case.

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Separable Reversible Data Hiding in Encrypted Images With High Capacity Based on Median-Edge Detector Prediction

Cited by 8 publications

References 25 publications

Cryptospace Invertible Steganography with Conditional Generative Adversarial Networks

Cryptospace Invertible Steganography with Conditional Generative Adversarial Networks

Digital Steganography and Watermarking for Digital Images: A Review of Current Research Directions

Embedding Biometric Information in Interpolated Medical Images with a Reversible and Adaptive Strategy

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