Diffusion models exhibit notable fragility when faced with adversarial prompts, and strengthening attack capabilities is crucial for uncovering such vulnerabilities and building more robust generative systems. Existing works often rely on white-box access to model gradients or hand-crafted prompt engineering, which is infeasible in real-world deployments due to restricted access or poor attack effect. In this paper, we propose CAHS-Attack, a CLIP-Aware Heuristic Search attack method. CAHS-Attack integrates Monte Carlo Tree Search (MCTS) to perform fine-grained suffix optimization, leveraging a constrained genetic algorithm to preselect high-potential adversarial prompts as root nodes, and retaining the most semantically disruptive outcome at each simulation rollout for efficient local search. Extensive experiments demonstrate that our method achieves state-of-the-art attack performance across both short and long prompts of varying semantics. Furthermore, we find that the fragility of SD models can be attributed to the inherent vulnerability of their CLIP-based text encoders, suggesting a fundamental security risk in current text-to-image pipelines.

Compared with cloud computing, edge computing can provide users with computing services that lower response latency and reduce bandwidth pressure, enabling jobs to be processed in the proximity of users. Due to diverse user requirements and limited network resources, it is crucial to guarantee user experience through efficient task scheduling. However, the complexity of network environments and the inherent dependencies between different tasks from each job make it challenging. Given the dynamicity of network conditions, computation resources and service requirements in wireless networks, we have provided a two-level elastic scheduling scheme for dependent task processing. First, a novel scheduling architecture is presented to decouple executor deployment and task assignment, facilitating the increase of resource utilization and the flexibility and elasticity of task scheduling. Then, an optimization problem has been formulated to minimize the execution delay of all jobs with consideration of the characteristics of network networks and task dependencies. After that, an enhanced first come first serve strategy has been employed to make task assignments based on the devised task sorting algorithm. Finally, extensive simulations have been done to evaluate the performance of our proposed algorithm, and the results have shown that our proposed algorithm performs better than benchmark algorithms.

The traditional data security systems have the problems, such as poor adaptability, technical barriers, and closed interfaces, which cannot meet the development requirements of beyond 5G (B5G) and the Internet of Things (IoT). In this article, we design a novel deployment network framework for adaptive security by using blockchain technology in the software defined network (SDN)-enabled mobile edge computing (MEC) system. The blockchain is deployed on multiple SDN servers, ensuring decision consistency and data security. The distributed SDN controllers can schedule and combine atomic security functions (ASFs) from the security resource pool of the MEC system to provide comprehensive security services. Furthermore, we developed a multiagent deep deterministic policy gradient (MADDPG) scheduling optimization algorithm to enhance the utility of our model while optimizing latency and energy cost. Simulations indicate that the algorithm successfully maximizes the overall utility of the MEC system, while adhering to the constraints of latency and energy cost.

Blockchain has been regarded as a trusted carrier for distributed data storage. With large volumes of valuable data stored on blockchain, data query has become a major requirement. However, the existing blockchains do not provide efficient query functionality because of their deep-rooted chain structure. Blockchain database is a new direction that constructs index on top of blockchain to provide rich query functionalities. The existing works are either insecure because the query process separates from the blockchain consensus, or inscalable because all the data needs to be stored in the block. In this paper, we propose a novel semantic blockchain database called MSTDB. We design a hybrid on/off chain blockchain storage architecture in which the majority of blockchain storage is offloaded to the off-chain storage and a novel index structure named Merkle Semantic Trie (MST) is designed to be a secure and semantic bridge between on- and off-chain. Based on MST, MSTDB provides a variety of semantic query functions including multi-keyword query, range query, Top-K query, and cross-chain query. To improve the performance further, we design some index compression and query preprocessing techniques for MSTDB. Extensive experiments demonstrate the effectiveness and efficiency of our blockchain database.

As a decentralized trusted database, the blockchain is finding applications in a growing number of fields such as finance, supply chain and medicine traceability, where large volumes of valuable data are stored on the blockchain. Currently, the mainstream blockchains employ a hybrid data storage architecture combining on-chain and off-chain storage. Real-time distributed search of mass data stored in this hybrid system is now a major need. However, previous fast retrieval schemes for the blockchain system are aimed only at on-chain data without considering their relevance to off-chain data, and thus fail to meet the requirement. In this paper, we propose an efficient blockchain data query scheme by introducing a novel Merkle Semantic Trie-based indexing technique without modifying the underlying database. A consensus on-chain index structure is constructed using the extracted semantic information of the off-chain data to create a mapping between the on-chain and off-chain data, thus enabling real-time data query both on and off the chain. Our scheme also provides multiple complex analytical query primitives to support semantic query, range query, and even fuzzy query. Experiments on three open data sets show that the proposed scheme has good query performance with shorter query latency for four different search types and offers better retrieval performance and verification efficiency than those available.