Abstract With the escalating proliferation of artificial intelligence technologies, AI-generated content (AIGC) has progressively permeated across diverse domains. However, this explosive application has also sparked widespread public discussion about the copyright of AIGC. Existing copyright legal frameworks, originally designed around human creators, now face a paradigm shift. As human involvement in the generation of AIGC diminishes, where creative expression increasingly hinges on AI. This discrepancy has introduced multifaceted complexities and challenges in determining the copyright ownership of AIGC within established legal boundaries. Given this, meticulous recording and auditing of contributions from all parties in AIGC generation becomes imperative. Blockchain, with its decentralized storage, offers a robust technical foundation for AIGC copyright management. Yet existing blockchain-based solutions have clear limitations: most only focus on certifying final generated products, ignoring the management of critical intermediate data across the full lifecycle, thus failing to meet the needs of core scenarios like copyright confirmation and multi-party profit distribution. For this purpose, this paper introduces AIGC-Chain, a trustworthy AIGC copyright management system. It conducts a comprehensive recording of intermediate data generated across the full lifecycle of AIGC. Such data is deposited into a decentralized blockchain for secure multi-party auditing, thereby constructing a trustworthy management for AIGC copyright. In copyright dispute scenarios, auditors can retrieve critical proof from the blockchain, facilitating precise determination of the copyright ownership of AIGC products. Both theoretical and experimental analyses confirm that this scheme shows exceptional performance and security in AIGC copyright management.

Blockchain strengthens copyright protection for AI generated content (AIGC) by establishing a transparent and traceable management framework, which encourages model providers to participate in forming an AIGC service system, collectively driving the development of AIGC and offering high-quality, reliable content generation services to a broader audience. Despite blockchain enabling full traceability in AIGC generation, some malicious model providers may exploit free riding actions by intercepting user requests and forwarding them to other providers to save computational resources or pursue greater profits, and then returning the generated product to users directly or with slight modifications. Such behavior hinders users from accessing high-quality AIGC service resources and threatens the legitimate rights of honest model providers, which may ultimately diminish their enthusiasm to participate in the AIGC service system, disrupting the balance of value co-creation within the system. To combat such free-riding behaviors and ensure equitable benefit distribution among participants, we propose a decentralized reputation-based model management approach within the blockchain-enabled AIGC service system, reducing the probability of malicious service providers par ticipating while providing users with a reliable reference for model selection. Moreover, it protects the content generation through a timestamp-based watermark to prevent malicious alteration and unauthorized use, safeguarding the interests of all participants during the generation process and enhancing the reliability and security of the AIGC service system. Experimental results demonstrate that the proposed approach can effectively constrain and supervise model behaviors, successfully combating free-riding actions in the AIGC service system, and providing a reliable and intuitive reference for users in model selection.

Modern business activities often rely on rewards programs as a common means of incentivizing consumers. However, in certain cases, it can be a lose-lose situation for both consumers and businesses. For consumers, operators may alter or terminate rewards programs. For operators, rewards programs are difficult to be trusted by consumers and can come with many potential costs. The metaverse is perceived as a utopian-style virtual social system, where brands or individuals, called as operators, can establish stores/stops to conduct business activities. That is, there are also rewards programs in the metaverse, and if people simply replicate existing programs, it may lead to the situations mentioned above. To this end, we aim to propose a win-win rewards program in the metaverse to alleviate the situation. It suggests that consumers can obtain items in rewards programs through self-assembly rather than through the redeem controlled by the operator. Operators cannot make modifications to the rewards program once it is launched. Therefore, this rewards program is trustworthy, and operators also divest themselves from the redemption process in the rewards program, thereby reducing many additional costs. Meanwhile, we implement a specific prototype from a technical perspective to match the proposed rewards program in the metaverse. It specifically implements the decomposition of items prepared by the operator into fragments. Once consumers obtain a sufficient number of fragments, they can assemble them into a complete item without the involvement of the operator.

The power supply system, as an indispensable electronic hardware module in most vehicles, needs the highest level of security and reliability to ensure the normal operation of the vehicle. Efficiently identifying any faulty battery at the earliest stage would prevent potential safety hazards. This paper aims to detect anomalous batteries using a time series analysis of their internal resistance. To identify the most meaningful patterns and extract their features, we propose a method named Pattern-based Vectorization for Time series (PVT). The PVT first encodes the local sequential shapes as unique symbols by sliding window, then maps each time series into a sequence of representative symbols, and finally generates the symbolic feature matrix for the whole time-series data via a TF-IDF statistical method. The effectiveness of PVT has been systematically evaluated with 7 classifiers on a large real civil aviation battery dataset collected from an uninterruptible power system. Our results show that PVT can significantly improve the performance of existing classifiers in detecting abnormal batteries. In particular, the combination with the RUBT classification model achieves the highest performance due to the random undersampling and boosting techniques that suit imbalanced data in anomaly detection scenarios.

Abstract Key‐based interval splitting arithmetic coding (KAC) possesses both encryption and compression capabilities. However, it possesses vulnerability to chosen‐plaintext attack because the attacker can explore the relationship between the key and the codeword to deduce the secret key. In order to resist this attack, we propose to introduce perturbation into KAC. The perturbation‐based KAC not only avoids the flaw of KAC that the splitting keys are usually located at the endpoint of certain codeword or at the border of two codewords but also removes the restriction that the keys are only allowed in certain sub‐intervals, which result in great convenience to the key scheduler. In addition, based on generalized arithmetic coding using Generalized Luröth Series, we study the phase‐space splitting of a chaotic map for generalized KAC and suggest the generalized perturbation‐based KAC. This leads to the design of a joint compression and encryption scheme with more powerful cryptographic features. Copyright © 2015 John Wiley & Sons, Ltd.