Future Internet

Deepfake detection in video data typically relies on mining deep embedded representations across multiple modalities to obtain discriminative fused features and thereby improve detection accuracy. However, existing approaches predominantly focus on how to exploit complementary information across modalities to ensure effective fusion, while often overlooking the impact of noise and interference present in the data. For instance, issues such as small objects, blurring, and occlusions in the visual modality can disrupt the semantic consistency of the fused features. To address this, we propose a Multimodality Semantic Consistency Fusion model for video forgery detection. The model introduces a semantic consistency gating mechanism to enhance the embedding of semantically aligned information across modalities, thereby improving the discriminability of the fused representations. Furthermore, we incorporate an event-level weakly supervised loss to strengthen the global semantic discrimination of the video data. Extensive experiments on standard video forgery detection benchmarks demonstrate the effectiveness of the proposed method, achieving superior performance in both forgery event detection and localization compared to state-of-the-art approaches.

This paper introduces a cybersecurity framework that combines a deception-based ransomware detection system, called the Intrusion and Ransomware Detection System for Cloud (IRDS4C), with a blockchain-enabled Cyber Threat Intelligence platform (CTIB). The framework aims to improve the detection, reporting, and sharing of ransomware threats in cloud environments. IRDS4C uses deception techniques such as honeypots, honeytokens, pretender network paths, and decoy applications to identify ransomware behavior within cloud systems. Tests on 53 Windows-based ransomware samples from seven families showed an ordinary detection time of about 12 s, often quicker than tralatitious methods like file hashing or entropy analysis. These detection results are currently limited to Windows-based ransomware environments, and do not yet cover Linux, containerized, or hypervisor-level ransomware. Detected threats are formatted using STIX/TAXII standards and firmly shared through CTIB. CTIB applies a hybrid blockchain consensus of Proof of Stake (PoS) and Proof of Work (PoW) to ensure data integrity and protection from tampering. Security analysis shows that an attacker would need to control over 71% of the network to compromise the system. CTIB also improves trust, accuracy, and participation in intelligence sharing, while smart contracts control access to erogenous data. In a local prototype deployment (Hardhat devnet + FastAPI/Uvicorn), CTIB achieved 74.93–125.92 CTI submissions/min, The number of attempts or requests in each test was 100 with median end-to-end latency 455.55–724.99 ms (p95: 577.68–1364.17 ms) across PoW difficulty profiles (difficulty_bits = 8–16).

Mobile crowdsensing (MCS) is an emerging paradigm that enables cost-effective, large-scale, and participatory data collection through mobile devices. However, the open nature of MCS raises significant privacy and trust challenges. Existing reputation models have made progress in assessing the quality of contributions, but they still struggle to manage prolonged inactivity, which can lead to outdated scores that no longer reflect current engagement. To address these issues, this paper presents RBCrowd, a dynamic reputation management system based on a dual blockchain architecture. It consists of the Sensing Chain (SC), a public blockchain recording sensing tasks and results, and the Reputation Chain (RC), a consortium blockchain managing user reputation scores. To guarantee privacy, the framework limits identity verification to the RC, ensuring that data on the SC is stored without direct links to the worker. We paired this privacy mechanism with a reputation model that rewards consistent, high-quality contributions. The system updates reputation scores by first validating the specific task and then adjusting for historical engagement, specifically penalizing prolonged inactivity. We evaluate RBCrowd through simulations in realistic MCS scenarios, and the results show that our framework provides more effective dynamic trust management than existing models. It also achieves increased reliability and fairness while managing prolonged inactivity through adaptive penalties.