Phishing detection algorithm based on attention and feature fusion by ZHANG Sirui, YAN Zhiwei, DONG Kejun, YUCHI Xuebiao

“…To address the problem of escalating adversarial phishing technologies, a phishing detection algorithm based on the attention mechanism and feature fusion was proposed, and a hierarchical classification model was established. …”
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Feature-based enhanced boosting algorithm for depression detection by Muhammad Sadiq Rohei, Kasturi Dewi Varathan, Shivakumara Palaiahnakote, Nor Badrul Anuar

Subjects: “…Feature-based enhanced boosting algorithm…”
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Shot boundary detection algorithm based on ORB by Jiang-qi TANG, Lin-jiang XIE, Qing-sheng YUAN, Dong-ming ZHANG, Xiu-guo BAO, Wei Guo

“…Experi-ment results show that the proposed algorithm is effective to solve false and miss detection caused by the above problems, with a sharp rise in procession speed.…”
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SIFT Feature-Based Video Camera Boundary Detection Algorithm by Lingqiang Kong

“…Aiming at the problem of low accuracy of edge detection of the film and television lens, a new SIFT feature-based camera detection algorithm was proposed. …”
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Lightweight Detection Algorithm for Breast-Mass Features in Ultrasound Images by Taojuan Li, Wen Liu, Mingxian Song, Zheng Gu, Ling Hai

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Object Detection Algorithm Based on Feature Enhancement and Anchor-object Matching by LI Cheng-yan, ZHAO Shuai, CHE Zi-xuan

Subjects: “…object detection…”
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Research on Traffic Marking Segmentation Detection Algorithm Based on Feature Fusion by Zhonghe He, Zizheng Gan, Pengfei Gong, Min Li, Kailong Li

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Video inter-frame tampering detection algorithm fusing multiple features by Hui XIAO, Bin WENG, Tianqiang HUANG, Han PU, Zehui HUANG

Subjects: “…video tamper detection…”
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A feature enhancement FCOS algorithm for dynamic traffic object detection by Tuqiang Zhou, Wei Liu, Haoran Li

“…Finally, in the detection part, the feature expression ability of shallow network was further enhanced by multi-scale feature fusion module, and the effectiveness of the proposed algorithm was verified using Cityscapes dataset. …”
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Spoofing speech detection algorithm based on joint feature and random forest by Jiaqi YU, Zhihua JIAN, Jia XU, Lin YOU, Yunlu WANG, Chao WU

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Spoofing speech detection algorithm based on joint feature and random forest by Jiaqi YU, Zhihua JIAN, Jia XU, Lin YOU, Yunlu WANG, Chao WU

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Machine learning algorithm for intelligent detection of WebShell by Hua DAI, Jing LI, Xin-dai LU, Xin SUN

“…WebShell is a common tool for network intrusions,which has the characteristics of great harm and good concealment.The current detection method is relatively simple,and easy to be bypassed,so it is difficult to deal with complex and flexible WebShell.To solve these problems,a supervised machine learning algorithm was put forward to detect WebShell intelligently.By learning the features of existing WebShell and non-existing WebShell pages,the algorithm can make prediction of the unknown pages,and the flexibility and adaptability were both very good.Compared with the traditional WebShell detection methods,the experiment proves that the algorithm has higher detection efficiency and accuracy,and at the same time there is a certain probability to detect new types of WebShell.…”
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Fan Blade Crack Detection Algorithm Based on Multi-Scale Feature Fusion by Yongjun Qi, Hailin Tang, Altangerel Khuder

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An improved air conditioner label defect detection algorithm based on SURF features by Huizi ZHOU, Yuelin LIU, Qing LIU, Jianwu LI

“…Aiming at the bottleneck that deep learning algorithms are not compatible with device detection and new sample collection,as well as poor detection timeliness and generalization ability,a traditional template matching detection algorithm based on SURF features was proposed. …”
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Retracted: Detection Algorithm of Tennis Serve Mistakes Based on Feature Point Trajectory by Advances in Meteorology

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Robust Adversarial Example Detection Algorithm Based on High-Level Feature Differences by Hua Mu, Chenggang Li, Anjie Peng, Yangyang Wang, Zhenyu Liang

“…To address these challenges, this paper introduces a novel adversarial example detection algorithm based on high-level feature differences (HFDs), which is specifically designed to improve robustness against both attacks and preprocessing operations. …”
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Novel Matching Algorithm for Effective Drone Detection and Identification by Radio Feature Extraction by Teng Wu, Yan Du, Runze Mao, Hui Xie, Shengjun Wei, Changzhen Hu

“…To address this challenge, this paper proposes a novel drone detection and identification algorithm based on transmission signal analysis. …”
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A range spread target detection algorithm based on polarimetric features and SVDD by Qiang LI, Yuanxin YAO, Xiangqi KONG

“…Multi-polarization range high resolution radar is an important mean for ground target detection.In the echo formed by it, the target occupies multiple range cells and becomes an extended target.The traditional spread target detection method relies on energy, and the detection performance decreases when the signal-to-clutter ratio decreases.A spread target detection algorithm based on polarization decomposition features was proposed, which improved the detection performance under low signal-to-clutter ratio by using the difference of polarization scattering characteristics between target and clutter.Specifically, 16 kinds of polarization decomposition features were extracted to form feature vectors as detection statistics, and then support vector data description (SVDD) was used to obtain the detection threshold.When training the detection threshold, the polarization decomposition features of clutter were extracted as training data.In order to ensure the false alarm probability, two penalty parameters were introduced into the objective function of SVDD.The experimental results show that the proposed method requires a signal-to-clutter ratio of about 12.6 dB in the case of Gobi background, false alarm probability of 10^-4 and detection probability of 90%, which is about 1.7 dB lower than the energy-based methods.…”
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A Feature-Enhanced Small Object Detection Algorithm Based on Attention Mechanism by Zhe Quan, Jun Sun

“…However, due to factors such as the environment, lighting, altitude, and angle, UAV images face challenges like small object sizes, high object density, and significant background interference, making object detection tasks difficult. To address these issues, we use YOLOv8s as the basic framework and introduce a multi-level feature fusion algorithm. …”
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Image forgery detection algorithm based on U-shaped detection network by Zhuzhu WANG

“…Aiming at the defects of traditional image tampering detection algorithm relying on single image attribute,low applicability and current high time-complexity detection algorithm based on deep learning,an U-shaped detection network image forgery detection algorithm was proposed.Firstly,the multi-stage feature information in the image by using the continuous convolution layers and the max-pooling layers was extracted by U-shaped detection network,and then the obtained feature information to the resolution of the input image through the upsampling operation was restored.At the same time,in order to ensure higher detection accuracy while extracting high-level semantic information of the image,the output features of each stage in U-shaped detection network would be merged with the corresponding output features through the upsampling layer.Further the hidden feature information between tampered and un-tampered regions in the image upon the characteristics of the general network was explored by U-shaped detection network,which could be realized quickly by using its end-to-end network structure and extracting the attributes of strong correlation information among image contexts that could ensure high-precision detection results.Finally,the conditional random field was used to optimize the output of the U-shaped detection network to obtain a more exact detection results.The experimental results show that the proposed algorithm outperforms those traditional forgery detection algorithms based on single image attribute and the current deep learning-based detection algorithm,and has good robustness.…”
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