As societal focus on image authenticity grows, image manipulation localization has become a crucial and challenging task in computer vision. Current methods relying on dual-stream encoders to extract features from both RGB and noise images often suffer from feature misalignment and information loss during fusion. Moreover, many localization methods use loss functions to identify manipulated areas, but balancing weights between manipulated regions and edges remains challenging. To address these challenges, we propose a novel method that integrates features in dual-stream networks with adaptive selective state spaces. By treating the two output features from the dual-stream encoder as system inputs, we construct a feature space that optimizes the system’s state space. Introducing temporal dynamics enriches the feature representation and enhances learning capabilities, significantly improving the accuracy and reliability of image manipulation localization. Additionally, we propose an edge residual review module that refines the boundaries of manipulated regions from the preliminary output, subsequently enhancing the input features for improved re-localization accuracy. Extensive experiments demonstrate that our approach yields competitive results on diverse large-scale image datasets, outperforming most state-of-the-art methods in both precision and robustness.

Federated learning (FL) enables multiple clients to collaboratively train machine learning (ML) models without exposing their private data. The recent surge in poisoning attacks and privacy leakage against FL has driven the development of secure federated learning (SFL) solutions. However, existing SFL schemes show inadequate performance when confronted with non-independent and identically distributed ( non-IID) data. In addition, traditional SFL architectures are prone to single point of failure (SPOF) issues due to the heavy computational and communication burdens imposed on a single server. In response to these issues, a novel blockchain-based secure federated learning (BSFL) framework is proposed in this paper. Specifically, we devise a proof of verification (PoV) consensus protocol to identify poisoning attacks under non-IID situations, while preventing the waste of computational and communication resources. Subsequently, we present a personalized differential privacy (PDP) mechanism, which achieves comprehensive privacy protection with lower noise levels. Furthermore, the integration of the blockchain with the proposed reward mechanism overcomes SPOF and fosters constructive participation through transparent processes. Formal theoretical analysis demonstrates the security, privacy, and efficiency of our framework. Extensive experimental evaluations indicate that BSFL exhibits strong resilience against various poisoning attacks and achieves better model accuracy compared to existing SFL solutions.

With the growing demand for outsourcing data to decentralized storage systems, ensuring the integrity of out-sourced data becomes a critical challenge. Existing auditing schemes, however, often assume single-copy or centralized models, and suffer from inefficiency, lack of public verifiability, or poor scalability in multi-replica settings. To address these limitations, we propose MPA, a lightweight and publicly verifiable auditing scheme tailored for multi-copy cloud storage. By integrating polynomial commitment schemes with Merkle trees, our design achieves efficient block-level integrity verification while enabling dynamic updates. To mitigate collusion between cloud service providers, each data copy is uniquely encrypted, and the audit process supports simultaneous verification across multiple providers. Furthermore, we introduce an optimized batch auditing mechanism that allows the verifier to aggregate proofs across different files and providers, reducing both computation and communication overhead. To enhance audit transparency and unpredictability, we adopt a blockchain-assisted challenge generation protocol based on commit-and-reveal randomness. Theoretical analysis and performance evaluation demonstrate that MPA achieves strong security guarantees under standard assumptions, while significantly outperforming existing solutions in terms of efficiency and scalability.

Data confidentiality, a fundamental security element for dependable cloud storage, has been drawing widespread concern. Public-key encryption with keyword search (PEKS) has emerged as a promising approach for privacy protection while enabling efficient retrieval of encrypted data. One of the typical applications of PEKS is searching sensitive electronic medical records (EMR) in healthcare clouds. However, many traditional countermeasures fall short of balancing privacy protection with search efficiency, and they often fail to support multi-user EMR sharing. To resolve these challenges, we propose a novel lightweight multi-user public-key authenticated encryption scheme with keyword search (LM-PAEKS). Our design effectively counters the inside keyword guessing attack (IKGA) while maintaining the sizes of ciphertext and trapdoor constant in multi-user scenarios. The novelty of our approach relies on introducing a dedicated receiver server that skillfully transforms the complex many-to-many relationship between senders and receivers into a streamlined one-to-one relationship. This transformation prevents the sizes of ciphertext and trapdoor from scaling linearly with the number of participants. Our approach ensures ciphertext indistinguishability and trapdoor privacy while avoiding bilinear pairing operations on the client side. Comparative performance analysis demonstrates that LM-PAEKS features significant computational efficiency while meeting higher security requirements, positioning it as a robust alternative to existing solutions.

Collaborative searchable encryption for group data sharing enables a consortium of authorized users to collectively generate trapdoors and decrypt search results. However, existing countermeasures may be vulnerable to a keyword guessing attack (KGA) initiated by malicious insiders, compromising the confidentiality of keywords. Simultaneously, these solutions often fail to guard against hostile manufacturers embedding backdoors, leading to potential information leakage. To address these challenges, we propose a novel privacy-preserving collaborative searchable encryption (PCSE) scheme tailored for group data sharing. This scheme introduces a dedicated keyword server to export server-derived keywords, thereby withstanding KGA attempts. Based on this, PCSE deploys cryptographic reverse firewalls to thwart subversion attacks. To overcome the single point of failure inherent in a single keyword server, the export of server-derived keywords is collaboratively performed by multiple keyword servers. Furthermore, PCSE extends its capabilities to support efficient multi-keyword searches and result verification and incorporates a rate-limiting mechanism to effectively slow down adversaries' online KGA attempts. Security analysis demonstrates that our scheme can resist KGA and subversion attack. Theoretical analyses and experimental results show that PCSE is significantly more practical for group data sharing systems compared with state-of-the-art works.