A novel copy-move forgery detection scheme using adaptive oversegmentation and feature point matching is proposed in this paper. The integrates both block-based keypoint-based methods. First, the algorithm segments host image into nonoverlapping irregular blocks adaptively. Then, points are extracted from each block as features, features matched with one another to locate labeled points; procedure can approximately indicate suspected regions. To detect regions more accurately, we propose...

Digital respiratory sounds provide valuable information for telemedicine and smart diagnosis in an non-invasive way of pathological detection. As the typical continuous abnormal sound, wheeze is clinically correlated with asthma or chronic obstructive lung diseases. Meanwhile, discontinuous adventitious crackle pneumonia, bronchitis, so on. The detection classification both attract many studies decades. However, due to contained artifacts constrained feature extraction methods, reliability...

Image-hashing-based tampering detection methods have been widely studied with continuous advancements. However, most of existing models are designed for a specific tampering. In this paper, we propose novel quaternion-based image hashing to detect almost all types tampering, including color changing, copy move, splicing, and so on. First, the quaternion Fourier-Mellin transform is used calculate geometric hash eliminate influence distortions. Then, new construction method, which combines...

This paper presents a low-cost graph-based traffic forecasting and congestion detection framework using online images from multiple cameras. The advantage of graph neural network (GNN) for is that it represents the in natural way. requires only surveillance cameras without any other sensors. It converts into two types data: volume image-based occupancy. A clustering-based construction method proposed to build based on network. For forecasting, models, including statistical models deep...

Change Detection of remote sensing images is an essential method for observing changes on the Earth's surface. Deep learning can efficiently process images. However, shallow features in data from different time are inherently inconsistent. During feature extraction stage, these mapped onto dimensional maps, giving rise to noise information. Existing algorithms ineffective dealing with effectively. This lead detection results being influenced by information, resulting fake detections. To...

Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated samples. Encouraging progress has been made for FSS by leveraging semantic features learned from base with sufficient training samples represent novel classes. The correlation-based methods lack the ability consider interaction of two subspace matching scores due inherent nature real-valued 2D convolutions. In this paper, we introduce quaternion perspective on correlation learning and propose...

The smart healthcare system plays a vital role in modern healthcare, facilitating the exchange of Electronic Patient Records (EPR) and improving medical care. Nonetheless, safeguarding inherent security confidentiality EPR data persists as formidable challenge, demanding rigorous attention innovative solutions. Digital watermarking safeguards genuineness integrity digital images is widely employed. In imaging, guaranteeing patient vital. this paper, we propose an...

A robust feature points detector for invariant audio watermarking is proposed in this paper. The segments centering at the detected are extracted both watermark embedding and extraction. These to various attacks will not be changed much maintaining high auditory quality. Besides, robustness inaudibility can achieved by into approximation coefficients of Stationary Wavelet Transform (SWT) domain, which shift invariant. spread spectrum communication technique adopted embed watermark....

In this paper, a new Quaternion Discrete Fourier Transform (QDFT)-based digital color image watermarking method is presented. addition, the QR (QQR) decomposition applied in technology for first time. First of all, QDFT and QQR are performed on host image, respectively, to acquire scalar part quaternion matrix watermark information embedding. After that, we divide generated by into blocks calculate entropy. The block with high entropy selected embed information. Then embedded extracted using...

To address the problem that existing quantum image watermarking schemes have only a single mode with weak robustness, in this paper we propose novel multi-modal (MMQW) scheme using generalized model of enhanced representation. Our provides four modes (G_G, G_C, C_C, C_G), covering both types grayscale and color images for watermark carrier image. enhance Block Bit-plane Centrosymmetric Expansion (BBCE) method, which utilizes controlled gates to extend watermark, making our method resistant...

In land cover change detection tasks, extracting universal features of changing targets is crucial for achieving precise results. A larger receptive field helps the model capture these targets. Although Large Kernel Convolution has been widely used in computer vision, its potential remote sensing images not fully explored. To address this, a novel Re-parameterization kernel Network Change Detection (CD-RLKNet) proposed. CD-RLKNet utilizes Spatial and Temporal Adaptive Fusion Module to...

Accurate bus arrival time prediction is crucial for enhancing passenger experience and optimizing smart city transit systems. Existing methods, typically based on single-route, sparse stop data, struggle with the complex spatiotemporal interactions present in dense areas multi-route networks, resulting lower accuracy. In this paper, we propose a frontend-enhanced time-series network, which Multi-Relational Modeling Graph Convolution (MRMGCN) as module, called FEN-MRMGCN. The proposed module...

Change detection is an important technique that identifies areas of change by comparing images the same location taken at different times, and it widely used in urban expansion monitoring, resource exploration, land use detection, post-disaster monitoring. However, existing methods often struggle with balancing extraction fine-grained spatial details effective semantic information integration, particularly for high-resolution remote sensing imagery. This paper proposes a image model called...