This paper illustrates the way a proposed quantum voting scheme can be designed in combination with a steganography technique called Least Significant Bit (LSB), by modifying a small number of pixels in multiple grayscale images. It combines the voting scheme with the novel enhanced quantum representation (NEQR) of an image, where the LSBs of these pixels represent the vote for each entity that takes part in the voting process. A server is also used, not only to count but also to guarantee the integrity of the votes (which is done inherently, by its design and quantum properties). The superdense coding circuit is part of the design, allowing each voter to use one qubit in order to transmit two classical bits (the vote value). The selected platform for testing this scheme is IBM Quantum Experience, together with the open-source framework called Qiskit (written in Python). This framework allows users to create various quantum circuits, using a wide selection of quantum gates, and then to simulate them, either on a simulator or on a real quantum device. The quantum circuits and the measurement results are also presented in this paper.
This paper illustrates the way a proposed quantum voting scheme can be designed in combination with a steganography technique called Least Significant Bit (LSB), by modifying a small number of pixels in multiple grayscale images. It combines the voting scheme with the novel enhanced quantum representation (NEQR) of an image, where the LSBs of these pixels represent the vote for each entity that takes part in the voting process. A server is also used, not only to count but also to guarantee the integrity of the votes (which is done inherently, by its design and quantum properties). The superdense coding circuit is part of the design, allowing each voter to use one qubit in order to transmit two classical bits (the vote value). The selected platform for testing this scheme is IBM Quantum Experience, together with the open-source framework called Qiskit (written in Python). This framework allows users to create various quantum circuits, using a wide selection of quantum gates, and then to simulate them, either on a simulator or on a real quantum device. The quantum circuits and the measurement results are also presented in this paper.
This paper presents a communication algorithm in which a grayscale image, shared between two parties, can be used to transmit a secret message, by applying the idea introduced in the B92 quantum protocol. The least significant qubits of the pixels’ representation in certain regions of the image are used. With the help of a server, the algorithm generates a random message, which can further act as a secret key for cryptographic algorithms in order to secure the data that two parties might want to exchange later on. The chosen representation of the image is NEQR (novel enhanced quantum representation) and the platform used for testing the described idea is IBM Quantum Experience, along with the open-source Python framework called Qiskit. This solution allows users to design, implement quantum circuits (containing various quantum gates), and simulate them using real devices and local simulators. An implementation using this platform for a sample image and the corresponding results are also presented in this paper.
This paper describes a novel steganography algorithm that combines quantum computing with the least significant bit technique (LSB). Using the quantum properties, along with the Python programming language and the Qiskit framework for the circuit simulation, a sub-image can be hidden inside a usual image. For a gray image, this article presents how the LSB of each of the first 16 pixels on the edges can be used to hide two threshold values, that are then used to filter out the image and reveal the secret message. The speedup, compared to the classical version, is possible due to the quantum representation of an image (NEQR is used in this paper) and the efficiency of the novel multi-bit quantum comparators.
In the realm of cybersecurity, the demand for robust encryption and secure data hiding techniques has intensified with the proliferation of digital communication and sensitive information exchange. This paper explores integration of QC advancements for enhancing the security of two critical cryptographic applications: Advanced Encryption Standard (AES) encryption and Least Significant Bit (LSB) image steganography. Quantum computing introduces the concept of qubits, which harness quantum mechanical phenomena to perform computations that classical computers struggle to achieve efficiently. By leveraging qubits, AES encryption can potentially benefit from enhanced key generation and encryption processes, thereby strengthening data confidentiality against conventional and future quantum attacks. Additionally, this paper investigates the application of quantum-inspired techniques to optimize the LSB method in image steganography. LSB embedding involves hiding data within LSB of image pixels, technique vulnerable to statistical attacks. Quantum-inspired approaches aim to mitigate these vulnerabilities by improving embedding efficiency and robustness without compromising image quality. Through theoretical analysis and simulation studies, this research evaluates the feasibility and potential benefits of integrating quantum computing into AES encryption and LSB steganography. The findings underscore the promise of quantum-enhanced methods in fortifying data security across digital platforms, laying the groundwork for future advancements in quantum cryptography and information hiding techniques.
Based on the NEQR representation of quantum grayscale images and binary images, the optimal LSBs-based quantum watermarking scheme is investigated in this paper, which embeds several binary images (watermark images) into a grayscale image (cover image). The size of the cover image and secret image are both assumed to be [Formula: see text]. Compared to quantum simple LSBs substitution method generating one stego-pixel, our presented quantum optimal LSBs-based method can generate three stego-pixel simultaneously first. Then one of them with lowest visual distortion is chosen as the final stego-pixel. To this end, first of all, the quantum circuits for a few basic quantum modules (i.e. Quantum Comparator, Parallel CNOT, Parallel Swap, ADDER MOD, Subtracter (SUB.ER) MOD and Absolute Value) are predefined. Following that, based on these simple modules, two composite quantum modules (i.e. the ADDER and SUB.ER MOD [Formula: see text] module and Choose final stego-pixel module) are further constructed. With the help of the basic and composite quantum modules, the integrated quantum circuit implementation of the optimal LSBs-based quantum watermark images embedding/extracting procedures are illustrated. Then, the experiment result are simulated under the classical computer software MATLAB 2014(b), which illustrates our presented optimal LSBs-based quantum watermarking methods are superior to the simple LSBs scheme in terms of PSNR and histogram graphs on the basis of visual effect, and the circuit’s complexity analysis also demonstrates our investigated scheme with a very low computational complexity. Finally, we analyze the security of quantum cryptography system, which verifies the quantum watermarking data can be securely transmitted to other legal normal users.
Abstract Quantum Image Representation (QIR) is a fundamental concept in quantum image processing. It describes how classical images are converted into quantum states for processing on quantum computers. Researchers proposed several QIR models, with FRQI and NEQR being the most popular ones. These models show the possibilities of the quantum image encoding. Nevertheless, they are also limited in some way. Mostly, pixel intensity accuracy, the required number of qubits, and circuit complexity have trade-offs. Their application to Noisy Intermediate-Scale Quantum (NISQ) devices becomes challenging, the quantum resources being small in such devices, due to this. In the following paper, we introduce our own approach, which is the Intensity-Preserving Quantum image Representation (IP-QIR). The key aim of IP-QIR is to maintain the information about the grayscale intensities and decrease the amount of quantum resources. IP-QIR quantifies pixel intensities by a controlled rotation scheme. The measurement statistics of a single qubit hold the intensity information and the position qubits represent the spatial information. The strategy uses small patches of images rather than complete images to make it easy to implement. This will decrease the depth of circuit design and makes the approach less advanced to the near-term quantum hardware. IBM Qiskit simulations are used in assessing the performance of IP-QIR. The three types of grayscale images of which experiments are run are the synthetic image patches, SAR images and medical TB chest X-ray images. It is revealed that IP-QIR maintains the intensity information compared to the FRQI and NEQR. SAR and medical data can reach values of fidelity of 84.12\%. The other notable point is that the size of a 4 x 4 image patch only needs five qubits which are much lower than in NEQR. Meanwhile, good reconstruction accuracy is achieved. These findings demonstrate the fact that IP-QIR is an effective and realistic approach to quantum image representation on the NISQ-era quantum devices.
In quantum image steganography, embedding capacity and security are two important issues. This paper presents a novel quantum steganography scheme using color images as cover images. First, the secret information is divided into 3-bit segments, and then each 3-bit segment is embedded into the LSB of one color pixel in the cover image according to its own value and using Gray code mapping rules. Extraction is the inverse of embedding. We designed the quantum circuits that implement the embedding and extracting process. The simulation results on a classical computer show that the proposed scheme outperforms several other existing schemes in terms of embedding capacity and security.