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Ever since the seminal work of Diffie and Hellman, cryptographic (cyclic) groups have served as a fundamental building block for constructing cryptographic schemes and protocols. The security of these constructions can often be based on the ...

Private set union (PSU) is a cryptographic protocol that allows two parties to compute the union of their sets without revealing anything else. Despite some efficient PSU protocols that have been proposed, they mainly focus on the balanced setting, where ...

Secure multiparty computation (MPC) protocols enable n parties, each with private inputs, to compute a given function without leaking information beyond the outputs. One of the main approaches to designing efficient MPC protocols is to use secret ...

The existing image segmentation tasks mainly focus on segmenting objects with specific characteristics, such as salient, camouflaged, and meticulous objects, etc. However, the research of highly accurate Dichotomous Image Segmentation (DIS) ...

• We design a novel model for dichotomous image segmentation (DIS) with two main modules: cross-scaling guidance (CSG) and semantic cross-transplantation (SCT). Extensive experiments on 4 challenging tasks of the DIS5K public benchmark show ...

With the rise of social media and online communities, information dissemination and content forwarding among users have become one of the topics of great interest in the field of social network analysis. Traditional information dissemination prediction ...

Circuit-based Private Set Intersection (circuit-PSI) empowers two parties, a client and a server, each with input sets X and Y, to securely compute a function f on the intersection X ∩ Y while preserving the confidentiality of X ∩ Y from both parties. ...

Existing learning-based methods for solving job shop scheduling problems (JSSP) usually use off-the-shelf GNN models tailored to undirected graphs and neglect the rich and meaningful topological structures of disjunctive graphs (DGs). This paper proposes ...

Recently, research based on the camouflaged object detection (COD) task has achieved great progress, while the collaborative camouflaged object detection (CoCOD) task is still lacking. Our research focuses on the simultaneous detection and ...

Clinical trials involve the collection of a wealth of data, comprising multiple diverse measurements performed at baseline and follow-up visits over the course of a trial. The most common primary analysis is restricted to a single, ...

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DeepFakes have raised serious societal concerns, leading to a great surge in detection-based forensics methods in recent years. Face forgery recognition is a standard detection method that usually follows a two-phase pipeline, i.e., it extracts the face ...

Smoke detection is essential for disaster management and timely response to fires. However, popular deep learning-based algorithms ignore key traditional features such as color and texture, causing limitations in detection. Therefore, a ...

• Propose a lightweight edge-guided smoke detection network, termed ESmokeNet.
• Feature extraction is enhanced by a mutual context embedding module (MCE).
• An anchor-free EdgeDet head pursues an effective fusion of edge cues and ...

360° videos are gaining popularity, but immersive analytics, particularly in object detection, confront challenges from complex scenes and high data volume. This imposes significant burdens on individual users and resource-limited edge devices. ...

Private set operations allow two parties to perform secure computation on their private sets, including intersection, union and functions of intersection/union. In this paper, we put forth a framework to perform private set operations. The ...

The purpose of camouflaged object detection (COD) focuses on detecting objects that seamlessly blend into their surroundings. Camouflaged objects pose a substantial challenge in the realm of computer vision due to various factors, including ...

• A cross-level refinement and interaction network (CRI-Net) focuses on camouflaged object.
• We earnestly design three key components for better study of COD missions to accurately predict camouflaged objects. The Semantic Amplification ...

The purpose of camouflaged object detection is to detect objects in images that are not easily perceived by human eyes. Aiming at the problems of low recognition performance and unsatisfied texture information extraction in the complex ...

• We design a novel model called LGPNet for collaborative camouflaged object detection.
• The Local Detail Mining module is designed to capture subtle information.
• the Global Intra-group Feature Extraction module uses global ...

Identifying safety hazards and ensuring quality control in construction sites requires information from surveillance images, such as clear detection boxes, accurate edges of targets, etc. Although both object detection and instance segmentation models ...

This paper focuses on the performance analysis of cyber–physical systems under the stealthy deception attack. Two types of attack models, strictly stealthy attack and ϵ-stealthy attack, are considered. The strictly stealthy deception attack model ...

Many animals actively change their own characteristics, such as color and texture, through camouflage, a natural defense mechanism, making them difficult to be detected in the natural environment, which makes the task of camouflaged object ...

The development of small UAV technology has led to the emergence of new challenges in UAV countermeasures. The timely detection of UAVs can effectively prevent potential infringements on airspace and privacy. Currently, methods based on deep learning ...

Proper inference of semantics is necessary for realistic image inpainting. Most image inpainting methods use deep generative models, which require large image datasets to predict and generate content. However, predicting the missing regions and generating ...