Dual Quantum Audio Watermarking Schemes Based on Quantum Discrete Cosine Transform

Chen, Kehan; Yan, Fei; Iliyasu, Abdullah M.; Zhao, Jianping

doi:10.1007/s10773-018-3950-9

Cited by 19 publications

(5 citation statements)

References 19 publications

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“…In order to encode () At into the relative phase of the qubit, a mapping operation is performed on it in accordance with Eq. (19).…”

Section: Secret Speech Embedding Processmentioning

confidence: 99%

“…In 2020, Javad et al [17] introduced quantum audio steganography using the QRDS model, which improved the embedding format from integer qubits to fractional qubits. Zhou et al [18], Chen et al [19], Li et al. [20], and Wang et al [21] employed positional scrambling, discrete cosine transform domain embedding, Gray code coding, and turtle shell algorithm to enhance the security, imperceptibility, capacity, and performance balance of the LSQb algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantum audio LSB steganography with entanglement-assisted modulation

Hao,

Yang,

et al. 2024

Quantum Inf Process

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This paper introduces two novel quantum audio steganography methods that incorporate the logical properties of entangled qubits into the least significant qubit steganography (LSQb) approach. The first method, called entanglement-assisted probability adjustment LSB (EP-LSQb), optimizes the superposition probability of the lowest qubits before embedding the information. This addresses the 'value-pairing' problem and improves anti-statistical analysis performance and embedding efficiency.The second method, called quantum phase direct embedding LSB (PD-LSQb), extends traditional LSB steganography by directly embedding the secret speech into the least significant qubit. Quantum circuits are constructed for both methods to demonstrate their feasibility. Theoretical analysis and simulation experiments show that these methods exhibit good imperceptibility ( PSNR 50  ) and embedding efficiency.Particularly, EP-LSQb method enhances the theoretical limit of embedding efficiency when compared to its corresponding classical counterparts. Additionally, due to the inclusion of entangled qubit particles, our methods offer competitive content security and noise robustness, distinguishing them from classical LSB or general LSQb methods and highlighting the unique advantages of quantum approaches.

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“…In order to encode () At into the relative phase of the qubit, a mapping operation is performed on it in accordance with Eq. (19).…”

Section: Secret Speech Embedding Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantum audio LSB steganography with entanglement-assisted modulation

Hao,

Yang,

et al. 2024

Quantum Inf Process

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“…where R ε (Δα ET /2) is defined in equation ( 4) and Δα ET /4 is the transitioning parameter for emotion types. Equation (16) shows the process for emotion-type transitions:…”

Section: Emotion-typementioning

confidence: 99%

“…Figure 7(c) presents the QC for emotion-intensity transition with a quantum adder and a quantum controlledset 1 gate [16] to transform the emotion intensity without affecting any emotion types. As shown in Figure 7(a), the quantum controlled-set 1 gate can be used to set an input qubit |i〉 to |1〉 with a condition |c〉 to prevent the overflow errors.…”

Section: Trust Admiration Interestmentioning

confidence: 99%

Emotion Generation and Transition of Companion Robots Based on Plutchik’s Model and Quantum Circuit Schemes

Yan

Yang

et al. 2021

Security and Communication Networks

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Loneliness and isolation are on the rise worldwide, threatening human well-being and the wellness of different age groups and backgrounds. Notably, global social distancing measures during the COVID-19 crisis have exacerbated this problem, resulting in various psychological and physiological ailments. Within both the categories of social and medical robots, companion robots are capable of engaging emotionally with users and providing continuous monitoring and assessment of their health. In this study, we propose a framework for modeling the emotion space of companion robots to facilitate their emotion generation and transition based on Plutchik’s wheel of emotions and reversible quantum circuit schemes. Superposition encodings allow fewer computing resources for the generation and storage of emotional states, and by using unitary operations, they facilitate easier emotion transition and recovery over different intervals. Further, an encryption strategy is designed based on the emotion communication architecture to secure the emotion-related data in human-robot interaction. It is hoped that such an integrative framework and research agenda exploring the role of companion robots will be useful to care for users’ social health by mitigating their negative emotions, especially during difficult times.

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“…Important uses of audio watermarking include copyright protection (proof of ownership), tamper detection, copy protection, finger printing, broadcast monitoring and information carrier. Also, due to the pervasiveness of quantum networks and quantum computing in recent years, the issue of audio watermarking in this field has been considered and papers such as references [6,7] have been presented for this purpose.…”

Section: Introductionmentioning

confidence: 99%

A novel audio watermarking scheme using ensemble-based watermark detector and discrete wavelet transform

Pourhashemi

Mosleh

Erfani

2020

Neural Comput & Applic

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Most existing extraction techniques in audio watermarking use conventional techniques in which some sets of special rules based on reverse embedding rules are used for watermark extraction and have many weaknesses, like very low robustness to destructive attacks. To overcome this problem, the use of machine learning-based methods has increased in recent years in this field. The disadvantage of these methods is the high reliance on a unique classifier and lack of proper efficiency when achieving high capacity, which is a major challenge in audio watermarking. The main purpose of this paper is to present a method that covers the weak points of conventional methods and simple intelligent methods and improves system performance using a synergistic combination of discrete wavelet transform (DWT) and ensemble-intelligent extraction approach by proposed combination of trained machine learning classifiers. For the embedding operation in the proposed method, the DWT and the difference in energy levels obtained through DWT coefficients are used. In the extraction section, three methods are used in parallel: (a) the trained support vector machine (SVM) classifier with RBF kernel, (b) trained SVM classifier with quadratic kernel and (c) the trained K-nearest neighbor classifier; finally, the majority function is used to vote and make a final decision to create an intelligent-based watermark detector. A training set is required to train the classifiers, whose bit sequence is generated by a proposed 5-bit linear-feedback shift register. The results of various experiments indicate that this ensemble method has achieved the appropriate imperceptibility and high capacity, along with higher robustness compared to conventional techniques and individual learning classifiers.

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Dual Quantum Audio Watermarking Schemes Based on Quantum Discrete Cosine Transform

Cited by 19 publications

References 19 publications

Quantum audio LSB steganography with entanglement-assisted modulation

Quantum audio LSB steganography with entanglement-assisted modulation

Emotion Generation and Transition of Companion Robots Based on Plutchik’s Model and Quantum Circuit Schemes

A novel audio watermarking scheme using ensemble-based watermark detector and discrete wavelet transform

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