Coalition Formation (CF) game emerges as a pioneering framework for the resource allocation in Unmanned Aerial Vehicles (UAVs) equipped with various types and complementary resources. However, both the overlapping-enabled collaborative CF and inter-coalition competitive behaviors significantly affect the system performance in complex multi-UAV scenarios. In this paper, we propose a Multiple Overlapping Coalitions (MOC) noncooperative game. Specifically, we first establish an optimization model encompassing coupled resource constraints. Subsequently, a task-priority-based incentive mechanism is designed to better motivate participation. To achieve the Nash equilibrium, a two-step solution technique incorporating relaxation and fine-tuning of resource granularity is designed. We propose a MOC noncooperative game-combined Multi-agent Proximal Policy Optimization (MAOPPPO). The simulation results substantiate that our approach outperforms the other five state-of-the-art learning countermeasures in terms of average reward with a gain of up to$4.59\%$after 800 of training episodes. In terms of throughput, the proposed MOC noncooperative game increases by$66.67\%$,$93.68\%$, and$11.76\%$compared with that of CF noncooperative game, non-CF noncooperative game, and consensus-based algorithm, respectively. For total resource contribution, the improvements are$62.99\%$,$94.59\%$, and$23.16\%$, respectively. The energy efficiency enhances by$6.82\%$,$23.68\%$, and$4.78\%$compared to the other three baselines, respectively.

The longest match strategy in LZ77, a major bottleneck in the compression process, is accelerated in enhanced algorithms such as LZ4 and ZSTD by using a hash table. However, it may results in numerous random memory accesses, which modern CPUs handle inefficiently, thus reducing the compression speed. In this paper, we introduce the LZHV algorithm, which significantly reduces unnecessary memory accesses by over 99% through the implementation of hash verification within the hash table. By integrating LZHV into LZ4 at its default compression level and ZSTD at levels 3 and 4, we achieve a compression speed improvement of over 10% across various platforms.

Video has become the primary channel for people to access information and engage in leisure and entertainment activities. Video service platforms have emerged to meet these demands and create significant profits. These platforms aggregate diverse videos and offer subscription services, aiming to provide convenient and personalized viewing experiences. To safeguard the copyright of video content against unauthorized access, these platforms employ digital rights management (DRM) technologies. However, the use of DRM faces significant security challenges due to the presence of malware or malicious users, which can compromise the decryption program running on user devices and lead to illegal video content distribution. This paper proposes a high-performance encryption scheme for video DRM protection. We establish the security of our proposed scheme through rigorous mathematical proof and analyze its effectiveness in the context of the video DRM scenario. Furthermore, the experimental results demonstrate that our scheme outperforms other schemes. Overall, our proposed scheme offers a promising solution to the security challenges faced by DRM technologies in protecting video content. Its efficiency, security, and resilience to various attacks make it a viable option for video service platforms seeking to enhance the security of their digital content.

The Internet of Things (IoT) has become a focus of information infrastructure development in recent years. The smart blockchain can provide various solutions for trust, security, and privacy (TSP) challenges to protect IoT data, and smart contracts are the foundation of blockchain intelligence, and greatly enhance the ability of smart blockchain to solve TSP problems. So, the security of smart contracts must be addressed. We propose an efficient smart contract vulnerability detector to improve the safety of smart contracts. It comprises a graph extraction method and a complete vulnerability detection process. The graph extraction method consists of vulnerability pattern extraction and a graph generation process. The vulnerability detection process first uses the approximate graph matching algorithm to select representative SCGraphs from the data set to build vulnerability SCGraph libraries. Second, determine whether the contract contains vulnerabilities by calculating the similarity between the SCGraphs generated from the contracts to be detected and the SCGraphs in the vulnerability library. Experiments show that our approach achieves an inspiring high detection rate and is the fastest among existing vulnerability detection tools, which indicates that it can provide good vulnerability detection for smart contracts.

The threshold digital signature technique is important for decentralized finance (DeFi) applications such as asset custody and cross-chain interoperations. The Edwards-curve digital signature algorithm (EdDSA) is widely used in blockchains, e.g., Libra/Diem; however, no suitable threshold solution exists. Therefore, to bridge this gap, we propose a threshold EdDSA that allows n parties to generate keys in a decentralized and distributed manner. Any t + 1-of-n parties can generate standard EdDSA signatures. This scheme supports an arbitrary threshold (t, n) and has been proven to be secure against at most t malicious adversaries. The theoretical analysis (computation complexity and communication footprints) and experimental results demonstrate that the proposed scheme performs efficiently on cloud servers and embedded devices. Furthermore, the proposed scheme is integrated with Tendermint, a blockchain framework that uses EdDSA, to generate keys and sign transactions in a decentralized manner, which indicates that this scheme is compatible with blockchains for supporting DeFi applications.

A visually meaningful image encryption scheme with an asymmetric structure based on the discrete wavelet transform (DWT) and Schur decomposition is proposed in this study. First, the RSA algorithm is used to generate the initial values for the chaotic system to produce the random sequence. Then, both scrambling and diffusion operations are performed on the plain image to obtain the preencrypted image. Moreover, the Schur decomposition is applied on the preencrypted plain image to obtain the upper triangular and orthogonal matrices. Second, the cover image is scrambled followed by a DWT operation. Four subbands are then formed, namely, LL, HL, LH, and HH. Finally, the former upper triangular matrix and orthogonal matrix are embedded into subbands LH and HH produced by the cover image. After the application of the inverse DWT and inverse scrambling operation, the final visually meaningful cover image embedded with a secret plain image can be obtained. No one can identify any useful information about the plain image from the final embedded cover image, nor can anybody know that there is any hidden secret image. Experimental simulations show that the normalized correlation values between the original cover image and the final visually meaningful cover image are approximately 0.9997. Therefore, the proposed encryption scheme is imperceptible for secret image communications.

We propose a new asymmetric pixel confusion algorithm for images based on the Rivest-Shamir-Adleman (RSA) public-key cryptosystem and Arnold map. First, the RSA asymmetric algorithm is used to generate two groups of Arnold transform parameters to address the problem of symmetrical distribution of Arnold map parameters. Second, the image is divided into blocks, and the first group of parameters is used to perform Arnold confusion on each sub-block. Then, the second group of parameters is used to perform Arnold confusion on the entire image. The image correlation is thereby fully weakened, and the image confusion degree and effect are further enhanced. The experimental results show that the proposed image pixel confusion algorithm has better confusion effect than the classical Arnold map based confusion and the row-column exchange based confusion. Specifically, the values of gray difference are close to one. In addition, the security of the new confusion operation is dependent on RSA, and it can act as one part of a confusion-substitution structure in a cipher.

This study proposes a new image encryption scheme based on a generalized Arnold map and Rivest–Shamir–Adleman (RSA) algorithm. First, the parameters of the generalized Arnold map are generated by an asymmetric encryption-system RSA algorithm, and the keystream is produced iteratively. To change the distribution of pixel values, the image data are hidden by XOR diffusion. Second, both rows and columns of the image are cyclically confused to hide the image data again. Then, the additive mode diffusion operation is performed to realize third-layer hiding for image content. The overall diffusion and confusion operations are conducted twice to obtain the final cipher image. Test results prove that the encryption scheme proposed in this study is effective and has strong antiattack capabilities and key sensitivity. In addition, because the scheme security relies on the RSA algorithm, it has high security.

This paper proposes a new image compression-encryption algorithm based on a meaningful image encryption framework. In block compressed sensing, the plain image is divided into blocks, and subsequently, each block is rendered sparse. The zigzag scrambling method is used to scramble pixel positions in all the blocks, and subsequently, dimension reduction is undertaken via compressive sensing. To ensure the robustness and security of our algorithm and the convenience of subsequent embedding operations, each block is merged, quantized, and disturbed again to obtain the secret image. In particular, landscape paintings have a characteristic hazy beauty, and secret images can be camouflaged in them to some extent. For this reason, in this paper, a landscape painting is selected as the carrier image. After a 2-level discrete wavelet transform (DWT) of the carrier image, the low-frequency and high-frequency coefficients obtained are further subjected to a discrete cosine transform (DCT). The DCT is simultaneously applied to the secret image as well to split it. Next, it is embedded into the DCT coefficients of the low-frequency and high-frequency components, respectively. Finally, the encrypted image is obtained. The experimental results show that, under the same compression ratio, the proposed image compression-encryption algorithm has better reconstruction effect, stronger security and imperceptibility, lower computational complexity, shorter time consumption, and lesser storage space requirements than the existing ones.

In this paper, an effective framework for chaotic encryption based on a three-dimensional logistic map is presented together with secure hash algorithm-3 (SHA-3) and electrocardiograph (ECG) signal. Following the analysis of the drawbacks, namely, fixed key and low sensitivity, of some current algorithms, this work tries to solve these two problems and includes two contributions: ( 1 ) removal of the phenomenon of summation invariance in a plain-image, for which SHA-3 is proposed to calculate the hash value for the plain-image, with the results being employed to influence the initial keys for chaotic map; ( 2 ) resolution of the problem of fixed key by using an ECG signal, that can be different for different subjects or different for same subject at different times. The Wolf algorithm is employed to produce all the control parameters and initial keys in the proposed encryption method. It is believed that combining with the classical architecture of permutation-diffusion, the summation invariance in the plain-image and shortcoming of a fixed key will be avoided in our algorithm. Furthermore, the experimental results and security analysis show that the proposed encryption algorithm can achieve confidentiality.

Abstract In this paper, a chaotic image encryption algorithm is suggested combining with double chaotic maps, SHA‐3 (Secure Hash Algorithm‐3), and auto‐updating system. To shuffle pixel positions, a matrix having same size of the plain‐image is generated dependent on the plain‐image in stage of permutation. Then, SHA‐3 is taken to calculate its hash values used to produce control parameter and initial condition of Logistic map. After that, total permutation is implemented for row and column to exchange pixels, where auto‐updating systems are established by different images acting like one‐time pad. Furthermore, 3D chaotic cat map is employed to enlarge key space in diffusion process. Permuted image is firstly divided into four sub‐blocks, which are queued randomly according to the key of Logistic map. The last sub‐block is picked out to calculate the hash values using SHA‐3, of which are assigned into three groups to update the initial conditions of 3D chaotic cat map performing like one‐time pad again. Current sub‐block is designed to influence next sub‐block in diffusion process. As a result, the proposed algorithm can resist efficiently the chosen‐plaintext and known‐plaintext attacks. Experimental results are also given to explain the high security of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract In this paper, because of the acquisition principle and big data of remote sensing image, we suggest an encryption scheme using chaotic system to build up the compressed sensing framework. A two‐dimensional generalized Arnold map is adopted here to generate the entries of the Toeplitz matrix used as measurement matrix. Then, the selecting row of the Toeplitz matrix is also decided by the chaotic sequence generated randomly by the Arnold map. With a further function of permutation operation, we can also encrypt remote sensing image after compression. The key space is constructed by the initial conditions in chaotic system. Experiment results show that the proposed method can be suitable to compressed sensing when the random measurement matrix is generated using chaotic system. Additionally, although the permutation operation is adopted, it is mainly used to enhance the security of cipher‐image. The reconstruction result is only dependent on the standard recovery algorithm. Copyright © 2015 John Wiley & Sons, Ltd.

This paper presents a novel image encryption scheme based on Toeplitz matrix and Hankel matrix. According to the definition and the expression of Toeplitz matrix and Hankel matrix, we permutate the positions of image pixels to confuse the relationship between the plain-image and cipher-image, and construct a new key. To enhance security further, hyper-chaos system of Lü and Chen is taken to change the grey distribution of image pixels simultaneously. Numerical experimental results demonstrate that the key space is large enough, and the key is sensitive to initial conditions to resist the brute-force attack in the proposed scheme. Additionally, the distribution of grey values in encrypted image has a random-like behavior to resist statistical analysis. The proposed encryption scheme in this paper can suggest a high secure and efficient way for real-time image encryption and transmission in practice.

Intuitionistic fuzzy set, was introduced by Atanassov, has been applied to many different fields, such as logic programming, pattern recognition, and decision making, etc. However, so far there has been few investigation on how to transform attribute tested values of alternative into a intuitionistic fuzzy number, and then complete decision making by intuitionistic fuzzy information. In this paper, the original attribute value (objective information) are characterized by crisp number which are given by decision maker. We define the concepts of supporting, opposing and neutral set of alternative respectively, develop an approach for transform attribute values into intuitionistic fuzzy number, and determine the order of alternatives based on the score and the degree of accuracy of the intuitionistic fuzzy number. Finally, a practical example is provided to illustrate the developed method.