Blockchain technology affords data integrity protection and building trust mechanisms in transactions for distributed networks, and, therefore, is seen as a promising revolutionary information technology. At the same time, the ongoing breakthrough in quantum computation technology contributes toward large-scale quantum computers, which might attack classic cryptography, seriously threatening the classic cryptography security currently employed in the blockchain. As a better alternative, a quantum blockchain has high expectations of being immune to quantum computing attacks perpetrated by quantum adversaries. Although several works have been presented, the problems of impracticality and inefficiency in quantum blockchain systems remain prominent and need to be addressed. First, this paper develops a quantum-secure blockchain (QSB) scheme by introducing a consensus mechanism—quantum proof of authority (QPoA) and an identity-based quantum signature (IQS)—wherein QPoA is used for new block generation and IQS is used for transaction signing and verification. Second, QPoA is developed by adopting a quantum voting protocol to achieve secure and efficient decentralization for the blockchain system, and a quantum random number generator (QRNG) is deployed for randomized leader node election to protect the blockchain system from centralized attacks like distributed denial of service (DDoS). Compared to previous work, our scheme is more practical and efficient without sacrificing security, greatly contributing to better addressing the challenges in the quantum era. Extensive security analysis demonstrates that our scheme provides better protection against quantum computing attacks than classic blockchains. Overall, our scheme presents a feasible solution for blockchain systems against quantum computing attacks through a quantum strategy, contributing toward quantum-secured blockchain in the quantum era.

The Internet of Things (IoT) is a network system that connects physical devices through the Internet, allowing them to interact. Nowadays, IoT has become an integral part of our lives, offering convenience and smart functionality. However, the growing number of IoT devices has brought about a corresponding increase in cybersecurity threats, such as device vulnerabilities, data privacy concerns, and network susceptibilities. Integrating blockchain technology with IoT has proven to be a promising approach to enhance IoT security. Nevertheless, the emergence of quantum computing poses a significant challenge to the security of traditional classical cryptography used in blockchain, potentially exposing it to quantum cyber-attacks. To support the growth of the IoT industry, mitigate quantum threats, and safeguard IoT data, this study proposes a robust blockchain solution for IoT that incorporates both classical and post-quantum security measures. Firstly, we present the Quantum-Enhanced Blockchain Architecture for IoT (QBIoT) to ensure secure data sharing and integrity protection. Secondly, we propose an improved Proof of Authority consensus algorithm called “Proof of Authority with Random Election” (PoARE), implemented within QBIoT for leader selection and new block creation. Thirdly, we develop a public-key quantum signature protocol for transaction verification in the blockchain. Finally, a comprehensive security analysis of QBIoT demonstrates its resilience against cyber threats from both classical and quantum adversaries. In summary, this research introduces an innovative quantum-enhanced blockchain solution to address quantum security concerns within the realm of IoT. The proposed QBIoT framework contributes to the ongoing development of quantum blockchain technology and offers valuable insights for future research on IoT security.

Constrained image splicing detection and localization (CISDL) is a newly formulated image forensics task that aims at detecting and localizing the source and forged regions from a series of input suspected image pairs. In this work, we propose a novel Scale-Adaptive Deep Matching (SADM) network for CISDL, consisting of a feature extractor, a scale-adaptive correlation module and a novel mask generator. The feature extractor is built on VGG, which has been reconstructed with atrous convolution. In the scale-adaptive correlation computation module, squeeze-and-excitation (SE) blocks and truncation operations are integrated to process arbitrary-sized images. In the mask generator, an attention-based separable convolutional block is designed to reconstruct richer spatial information and generate more accurate localization results with less parameters and computation burden. Last but not least, we design a pyramid framework of SADM to capture multiscale details, which can increase the detection and localization accuracy of multiscale regions and boundaries. Extensive experiments demonstrate the effectiveness of SADM and the pyramid framework.

With the rapid development of artificial intelligence and Internet of Things (IoT) technologies, automotive companies are integrating federated learning into connected vehicles to provide users with smarter services. Federated learning enables vehicles to collaboratively train a global model without sharing sensitive local data, thereby mitigating privacy risks. However, the dynamic and open nature of the Internet of Vehicles (IoV) makes it vulnerable to potential attacks, where attackers may intercept or tamper with transmitted local model parameters, compromising their integrity and exposing user privacy. Although existing solutions like differential privacy and encryption can address these issues, they may reduce data usability or increase computational complexity. To tackle these challenges, we propose a conditional privacy-preserving identity-authentication scheme, CPPA-SM2, to provide privacy protection for federated learning. Unlike existing methods, CPPA-SM2 allows vehicles to participate in training anonymously, thereby achieving efficient privacy protection. Performance evaluations and experimental results demonstrate that, compared to state-of-the-art schemes, CPPA-SM2 significantly reduces the overhead of signing, verification and communication while achieving more security features.

Permission-related issues in Android apps have been widely studied in our research community, while most of the previous studies considered these issues from the perspective of app users. In this paper, we take a different angle to revisit the permission-related issues from the perspective of app developers. First, we perform an empirical study on investigating how we can help developers make better decisions on permission uses during app development. With detailed experimental results, we show that many permission-related issues can be identified and fixed during the application development phase. In order to help developers to identify and fix these issues, we develop PerHelper, an IDEplugin to automatically infer candidate permission sets, which help guide developers to set permissions more effectively and accurately. We integrate permission-related bug detection into PerHelper and demonstrate its applicability and flexibility through case studies on a set of open-source Android apps.