A new locally optimum watermark detection using vector-based hidden Markov model in wavelet domain

Amini, Marzieh; Ahmad, M. Omair; Swamy, M.N.S.

doi:10.1016/j.sigpro.2017.01.019

Cited by 39 publications

(15 citation statements)

References 30 publications

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“…Furthermore, image steganalysis algorithms based on deep learning technology, such as Convolutional Neural Network (CNN) models, often use various network structures to learn the effective features of images to distinguish cover and stego images [18], thus proposing higher requirements for the detection resistant performance of steganographic algorithms. Therefore, how to balance the resilience of embedded messages under lossy channel [1] and the detection resistance of stego images [3] is a major problem for image steganography on mobile devices, which access multiple changing communication channels.…”

Section: Introductionmentioning

confidence: 99%

Enhancing reliability and efficiency for real-time robust adaptive steganography using cyclic redundancy check codes

Zhang¹,

Luo²,

Zhu³

et al. 2019

J Real-Time Image Proc

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The development of multimedia and deep learning technology bring new challenges to steganography and steganalysis techniques. Meanwhile, robust steganography, as a class of new techniques aiming to solve the problem of covert communication under lossy channels, has become a new research hotspot in the field of information hiding. To improve the communication reliability and efficiency for current real-time robust steganography methods, a concatenated code, composed of Syndrome-Trellis Codes (STC) and Cyclic Redundancy Check (CRC) codes, is proposed in this manuscript. Using its strong error detection capability, high coding efficiency and low embedding costs, an enhanced robust adaptive steganography framework and three adaptive steganographic methods resisting JPEG compression and detection are proposed. On this basis, to provide a theoretical reference for message extraction integrity, the fault tolerance of the proposed steganography methods is analyzed using the residual model of JPEG compression, thus obtaining the appropriate coding parameters. Experimental results show that the proposed methods have a significantly stronger robustness against compression, and are more difficult to be detected by statistical based steganalytic methods comparing with existing robust steganography methods.

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Section: Introductionmentioning

confidence: 99%

Enhancing reliability and efficiency for real-time robust adaptive steganography using cyclic redundancy check codes

Zhang¹,

Luo²,

Zhu³

et al. 2019

J Real-Time Image Proc

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show abstract

“…The main aim of digital watermarking is to provide copyright safety. Under the application of this model, the scope is progressively developed along with copyright security, copy management, fingerprint analyzing, content authorization, broadcast observation, video monitoring, and so forth [3]. At the same time, the application objects are expanded from traditional digital images to alternate applications like videos, texts, speech etc.…”

Section: Introductionmentioning

confidence: 99%

“…Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD), Contourlet Transform and hidden markov model (HMM) in wavelet domain has been presented [3]. Therefore, watermarking methods are dependson discrete Fourier transform (DFT) experiences cropping intrusions and if the aspect ratio is modified, it is impossible for watermark to retain the modifications as it affects the image frequency content.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Chaotic Encryption with Video Watermarking Technique using Improved Whale Optimization Algorithm

2020

IJATCSE

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Due to the advancements and familiarity of the Internet and the rise of different live broadcasting environments, digital videos have become a part of every day's life. On the other hand, security of videos poses a major threat in the common public networks. Digital watermarking is an effective approach commonly used to secure the digital information. Watermarking is a process of hiding data into multimedia content with no loss of the quality of the content in such a way that the watermark can be extracted by the legal user. This paper proposes a new digital video watermarking with encryption (DVWE) technique using improved whale optimization algorithm. The proposed model initially splits the input video into a sequence of frames and watermark data is hidden into every individual frame. Then, the image frames are decomposed using multi-level discrete wavelet transform (DWT). Besides, improved whale optimization algorithm (IWOA) is derived by incorporating the benefits of whale optimization algorithm (WOA) with levy flight (LF) concept for optimal pixel selection in the cover image. Followed by, the R, G and B components in the watermark data is encrypted by three encryption techniques namely elliptic curve cryptography (ECC), double logistic chaotic map (DLCM) and signcryption. As a result, the encrypted watermark image data is embedded into the cover image and then converted into a watermarked video. At the receiving end, the reverse process takes place to retrieve the secret watermark video. In order to assess the performance of the proposed model, an extensive experimental results analysis takes place. The simulation outcome depicted the effective performance of the presented model over the existing models.

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“…• Choosing coefficients in a specific subband for embedding the watermark: e.g., embedding in high frequency subbands for better imperceptibility [9,22,28,33,42]; embedding in low frequency subband to achieve high robustness [49,50] or the approaximation subband with the maximum variance [6,8] and balancing imperceptibility and robustness with all subbands spread spectrum embedding [17,38]. • Using different wavelet kernels: e.g., Haar or other Daubechies family orthogonal wavelets [5,9,10,28,33,49] and biorthogonal wavelets [50].…”

Section: Introductionmentioning

confidence: 99%

“…• Using different wavelet kernels: e.g., Haar or other Daubechies family orthogonal wavelets [5,9,10,28,33,49] and biorthogonal wavelets [50]. • Optimising the host coefficient selection: e.g., choosing all coefficients in a subband [8,9,17]; using a threshold based on their magnitude significance [22]; the just noticeable difference(JND) [42,50]; a mask based on the Human Visual System (HVS) model [6,7,9,36]; a fusion rule-based mask for refining the selection of host coefficients [10] and blind re-quantization of a coefficient with respect to a group of coefficients within a given window [28,33,38,49]. Though many independent algorithms are available in the literature, a gap, that requires a generalized mathematical analysis to identify the relationship between distortion performance and various wavelet-based watermarking parameters responsible for embedding distortion, was identified.…”

Section: Introductionmentioning

confidence: 99%

Embedding Distortion Analysis in Wavelet-domain Watermarking

Bhowmik

Abhayaratne

2019

ACM Trans. Multimedia Comput. Commun. Appl.

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Imperceptibility and robustness are two complementary fundamental requirements of any watermarking algorithm. Low strength watermarking yields high imperceptibility, but exhibits poor robustness. High strength watermarking schemes achieve good robustness but often infuse distortions resulting in poor visual quality in host image. This paper analyses the embedding distortion for wavelet based watermarking schemes. We derive the relationship between distortion, measured in mean square error (MSE), and the watermark embedding modification and propose the linear proportionality between MSE and the sum of energy of the selected wavelet coefficients for watermark embedding modification. The initial proposition assumes the orthonormality of discrete wavelet transform. It is further extended for non-orthonormal wavelet kernels using a weighting parameter, that follows the energy conservation theorems in wavelet frames. The proposed analysis is verified by experimental results for both non-blind and blind watermarking schemes. Such a model is useful to find the optimum input parameters, including, the wavelet kernel, coefficient selection and subband choices for wavelet domain image watermarking. CCS Concepts: • Security and privacy → Digital rights management.

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A new locally optimum watermark detection using vector-based hidden Markov model in wavelet domain

Cited by 39 publications

References 30 publications

Enhancing reliability and efficiency for real-time robust adaptive steganography using cyclic redundancy check codes

Enhancing reliability and efficiency for real-time robust adaptive steganography using cyclic redundancy check codes

An Efficient Chaotic Encryption with Video Watermarking Technique using Improved Whale Optimization Algorithm

Embedding Distortion Analysis in Wavelet-domain Watermarking

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