Recent advances in deep learning have significantly improved road extraction from high-resolution remote sensing (RS) images, yet challenges persist in scenarios with severe obstructions (e.g., forest canopies or dense fog) where semantic information is highly limited. To address these issues while overcoming the parameter inefficiency and suboptimal performance of existing multi-modal approaches, we propose a lightweight Cross-modal Frequency-domain Interaction and Fusion network (CFIFNet) for effective interaction and fusion between high-resolution RS images and GPS trajectory/Light Detection and Ranging (LiDAR) images based on the frequency domain. To facilitate accurate extraction of highly similar road-background edge regions, the model incorporates the Wavelet Interaction Module (WIM), which uses discrete wavelet transform to decompose multi-modal inputs into high/low-frequency components—with high-frequency features undergoing global interaction via Transformer and low-frequency components undergoing local channel-spatial dual-branch interaction. Additionally, the Fourier Frequency-domain Fusion (FFF) module enables efficient fusion through joint amplitude-phase manipulation, generating feature maps with enhanced semantic discrimination to improve detection of small roads and road-like objects. Moreover, the Adaptive Frequency Convolution Attention (AFCA) is integrated to alleviate road edge occlusion by serially enhancing road features via dual-domain (channel and frequency) refinement. Evaluation metrics and visualization results on multiple multi-modal road datasets validate CFIFNet’s effectiveness, and a series of experiments demonstrate its efficiency in aligning cross-modal features, avoiding inter-modal noise interference, and achieving complementary advantages in multi-source road scenes.

Trusted execution environments based on RISC-V architecture like Keystone remain susceptible to leaking page access patterns of applications via simple demand paging, in which a malicious Operating System (OS) deduces sensitive information from it. To address this issue, Keystone requires protecting sensitive access patterns from being revealed to the malicious OS by implementing oblivious demand paging. In this paper, we use Oblivious RAM (ORAM) techniques that obfuscate access patterns while simultaneously making demand paging oblivious for Keystone. Furthermore, we present customized optimizations to Ring ORAM, aimed at minimizing the performance overhead incurred by applications during both secure and unsecure demand paging in Keystone. These optimizations encompass strategies such as encoding the position map within the page table, utilizing a resizable tree structure and selective eviction of only the root bucket. These improvements collectively contribute to minimizing performance slowdown. We implement and evaluate our optimized Ring ORAM for oblivious demand paging, which shows the average performance slowdown of 7.1x in comparison to the simple Ring ORAM slowdown of 26.2x.

Processors are an important component of computer systems, but resource sharing in space and time, as well as performance first design concepts, result in a series of security issues for processors. On the one hand, processor security evaluation can systematically analyze and verify the security of the processor, deduce the key reasons for security risks, and on the other hand, it can assist in processor design, verifying processor security at a lower cost at the beginning of the design, compared to later software and hardware protection.This paper proposes a module level security evaluation method based on model checking, modeling the module as a mealy finite state machine to analyze the relationship between its outputs, inputs and states. Computational Logic Tree (CTL) is used to represent possible execution paths, and all paths are traversed to derive counterexample paths to represent possible attack paths and information leakage processes. We use the Common Vulnerability Scoring System(CVSS) to score each counterexample path. Based on these counterexample paths and CVSS scores, we ultimately obtained a total risk score to represent the security of the module. We conduct a case study on Cache to verify the effectiveness of our proposed method.

The design of secure cache architectures within computer systems primarily aims to mitigate side-channel attacks and minimize the risks of information leakage. However, verifying the effectiveness of secure cache designs introduces numerous challenges. The assessment of cache architecture security in prior research has mainly been based on the evaluators’ expertise, which lacks convincing evidence. Therefore, it is imperative to establish a universal and comprehensive formal verification methodology to evaluate the security of cache designs. This paper analyzes the advantages and disadvantages of an existing formal verification method. Based on this analysis, we introduce an enhanced formal verification method that utilizes noninterference hyperproperties to verify the security of cache architectures.In this paper, an extended triple mutual information formula is utilized to verify the satisfaction of noninterference hyperproperties within cache architectures and identify potential information leakages through three independence conditions. The degree of information leakage is evaluated by measuring the dependencies between the victim’s inputs and the attacker’s observations through triple mutual information. This paper instantiates existing cache architectures and reveals potential vulnerabilities by formalizing the behavior specification and replacement policy of a cache as an extended abstract state machine. Lastly, the cache security structure is formally validated utilizing the proposed security model, with the aim of evidencing its effectiveness and soundness.

Influenced by the concepts of deep learning, extracting roads from high-resolution remote sensing scenes has gained significant attention. However, there are still limitations in both metrics and practical application scenarios. To address these limitations, we proposed a deformable and dynamic cross-transformer network (DDCTNet), introducing three key innovations. Firstly, we employed a deformable and dynamic cross-transformer (DDCT) attention module to enhance the recovery of data and structural information during the feature map upsampling by providing rich semantic information of encoding stage to decoding stage from spatial and channel dimensions, respectively, which improved the quality of upsampling while preserving the inherent characteristics of the road. Secondly, we introduced a cross-scale strip-pooling axial attention (CSSA) between discontinuous encoding stages to alleviate the information loss caused by down-sampling and highlight the linear characteristic of roads by leveraging rich semantic information from previous stage, which not only considers road linear features in complex scenes but also reduces computational complexity. Finally, we designed an auxiliary head (AuxHead) by fusing the outputs from the latter three decoding modules to enhance the model’s generalization performance and convergence speed. Extensive experiments were conducted on three benchmark datasets. We also compared our DDCTNet with other classic road extraction models. The results show a noticeable improvement of 1%-5% across various evaluation metrics in three datasets. Additionally, the visualized results demonstrate that the proposed DDCTNet provides more accurate representations of real road scenes including distinguishing regions with high foreground-background similarity, addressing road occlusion, etc.

Influenced by deep learning, extracting roads from high-resolution remote sensing images has attracted extensive attention. However, most previous works have focused on fully supervised models relying on large amounts of annotated data and have not considered the characteristics of narrow and elongated roads. In order to alleviate the model’s dependency on labeled data, reduce annotation workload, and fully exploit road characteristics, we proposed a semi-supervised road extraction network via multiple consistency and multitask constraints (MCMCNet) that utilizes only minimal labeled data, while exploiting unlabeled data through the mining of pseudo-label information for constraint. Moreover, to ensure the generation of more accurate pseudo-labels, we incorporated a guided contrastive learning module (GCLM) into the model to increase interclass discriminability and enhance consistency constraints. In addition, to ensure the continuity of road extraction and integrity of the main roads, we added a road skeleton (road centerline) prediction head (RSPH) in addition to the original road segmentation prediction head. Finally, we introduced an adaptive road augment module (ARAM) to enhance linear road features and avoid learning redundant information by the use of local and global information adapted to road features. Extensive experiments demonstrated that MCMCNet achieved a 3%–5% improvement in$F1$and IoU across three benchmark datasets, compared to other classical semi-supervised road extraction models, and the visualization results confirmed that MCMCNet partially addressed challenges including road occlusion, foreground-background high-similarity regions at extremely low label rates. The code is available athttps://github.com/zhouyiqingzz/MCMCNet.

Intel Software Guard Extensions (SGX) protect sensitive content of applications on the cloud platform by creating an isolated environment on an untrusted operating system. However, resent works have shown that the SGX is vulnerable to a variety of side channel attacks which could be severely damage the data confidentiality provided by SGX, such as the cache side channel attack. Unfortunately, existing defense mechanisms either provide an incomplete protection or incur too much performance costs. In this paper, we propose a defense countermeasure against cache side channel attacks for SGX by detecting abnormal each level cache use behaviors. We create auxiliary threads for each enclave thread and detect when asynchronous enclave exits (AEX) occur, which defeats the condition of L1/L2 cache side channel attacks that attacker and victim threads execute in the same physical core. We put some guard data to the cache lines and inspect access time, which detects last level cache eviction set behaviors. More importantly, we utilize optimizations to reduce the performance overhead caused by AEX detection. In comparison to existing approaches, our design is secure against any cache level side channel attacks and its performance loss increases less.

Shared resources facilitate both side and covert channels and greatly endanger information security even in cloud environments. In cloud computing environments, multiple tenants often reside on the same multi-processor system. Therefore, it is important to prevent information leakage between tenants. Shared memory between tenants is usually disabled for security reasons. In addition, tenants typically do not share physical CPUs. In this case, cache attacks do not work. As a common shared resource, DRAM memory can also be exploited as a source of side and covert channels.In this paper, Computation Tree Logic (CTL) is used to model the behaviors of row buffer logic in DRAM and derive all possible timing-based vulnerabilities. The problem of state space explosion is alleviated by using bounded model checking in this method. In total, our method derives 24 kinds of DRAM timing-based vulnerabilities. Furthermore, we analyze DRAM vulnerabilities to help engineers understand them and take corresponding measures in the design according to derived security specifications.

Virtual reality (VR) not only expands the learning field and enriches the learning experience, but also provides an effective presentation for scarce educational resources. However, because VR mainly depends on image processing technology, its application in the field of education mostly stays at the level of knowledge observation, and many challenges are gradually exposed, such as untimely interaction and weak sense of existence. In recent years, haptics has gradually become the development frontier of human-computer interaction, breaking through the limitation of single visual stimulation of VR to a great extent. Some researchers began to explore the teaching mode of VR with haptics, in order to mobilize learners’ multi-sensory stimulus responses such as hearing, touch, force and movement, and enhance learners’ on-the-spot experience and flow experience. In order to deeply explore the effectiveness of VR with haptics in teaching and explore the regulatory variables affecting this teaching method, 1646 academic papers published from 2010 to 2020 were screened based on WOS and Scopus databases. After meta-analysis of 50 selected research data, it is found that: (1) from the perspective of overall effect, VR with haptics can significantly improve learners’ learning performance and efficiency; (2) Through the analysis of the effects of three regulatory variables, it is found that VR with touch has a positive impact on learning performance; VR with haptics has a positive impact on learning efficiency, especially for learners who have no previous learning experience and take practical skills as their learning goal for a long time. This discovery provides an important data reference for building an effective “virtual reality with haptics” teaching scene.

Inferring emotions from image social networks is a hot research topic nowadays. For image social networks (Flickr, Instagram), there is an interesting phenomenon that people would like to establish or attend virtual groups and share images with different topics and emotions in different groups. Previous researches on inferring emotions usually focus on image content and user personalization, thus leading an interesting but challenging problem: whether virtual groups can influence members(users)` emotions. In this paper, we systematically study this problem from two aspects: 1) whether group homophily in users' emotions exists in image social networks; 2) how to model this subtle and complex group homophily in image social networks. Inspired by the study results of two aspects, we introduce group information to infer emotions in image social networks, and propose a novel Group-Based Factor Graph Model (G-FGM), incorporating image content, user personalization and group information to understand the emotions behind social images better. The experimental results on a dataset containing 218, 816 emotion-labeled images from Flickr show that our model outperforms (8.6-19.4% improvement in terms of F1-Measure) several baseline methods.