scholarly journals Shallow and Deep Feature Fusion for Digital Audio Tampering Detection

2021 ◽  
Author(s):  
Chunyan Zeng ◽  
Yao Yang ◽  
Zhifeng Wang ◽  
Shuai Kong ◽  
Shixiong Feng ◽  
...  
Keyword(s):  
Instantaneous Frequency ◽  
Feature Fusion ◽  
Audio Signal ◽  
Detection Accuracy ◽  
Digital Audio ◽  
Tampering Detection ◽  
Deep Feature ◽  
Band Pass ◽  
Feature Expression ◽  
Fully Connected

Abstract Digital Audio tampering detection can be applied to verify the authenticity of digital audio. However, the current methods are mostly based on visual comparison analysis of the continuity of electronic network frequency (ENF) of digital audio with a standard ENF database. It is usually tricky to obtain the ENF database, and the feature expression of the visualization method is weak, which leads to low detection accuracy. In order to solve this problem, this paper proposed an audio tampering detection method based on the fusion of shallow and deep features. Firstly, the band-pass filtering process is performed on the audio signal to obtain the ENF components, and then the discrete Fourier transform and Hilbert transform are applied to obtain the phase and instantaneous frequency of the ENF components. Secondly, the shallow features are extracted by performing framing and fitting operations on the estimated phase and instantaneous frequency. Then, the designed convolutional neural network is used to obtain deep features, and the attention mechanism is applied to fuse shallow features and deep features. Finally, after dimensionality reduction through the fully connected layer, the Softmax layer is used for classification to detect the tampering audio. The method achieves 97.03% accuracy on three classic databases, which are Carioca 1, Carioca 2, and New Spanish. In addition, we have achieved an accuracy of 88.31% on the newly constructed database GAUDI-DI. Experimental results show that the proposed method is superior to the state-of-the-art method.

scholarly journals LPI Radar Waveform Recognition Based on Multi-Resolution Deep Feature Fusion

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 26138-26146
Author(s):  
Xue Ni ◽  
Huali Wang ◽  
Fan Meng ◽  
Jing Hu ◽  
Changkai Tong
Keyword(s):  
Feature Fusion ◽  
Deep Feature

scholarly journals Multi-Feature Fusion for Weak Target Detection on Sea-Surface Based on FAR Controllable Deep Forest Model

2021 ◽  
Vol 13 (4) ◽  
pp. 812
Author(s):  
Jiahuan Zhang ◽  
Hongjun Song
Keyword(s):  
Target Detection ◽  
Feature Fusion ◽  
Feature Space ◽  
Sea Surface ◽  
Detection Accuracy ◽  
Forest Model ◽  
High Profile ◽  
Deep Forest ◽  
Positive Effect ◽  
Weak Target

Target detection on the sea-surface has always been a high-profile problem, and the detection of weak targets is one of the most difficult problems and the key issue under this problem. Traditional techniques, such as imaging, cannot effectively detect these types of targets, so researchers choose to start by mining the characteristics of the received echoes and other aspects for target detection. This paper proposes a false alarm rate (FAR) controllable deep forest model based on six-dimensional feature space for efficient and accurate detection of weak targets on the sea-surface. This is the first attempt at the deep forest model in this field. The validity of the model was verified on IPIX data, and the detection probability was compared with other proposed methods. Under the same FAR condition, the average detection accuracy rate of the proposed method could reach over 99.19%, which is 9.96% better than the results of the current most advanced method (K-NN FAR-controlled Detector). Experimental results show that multi-feature fusion and the use of a suitable detection framework have a positive effect on the detection of weak targets on the sea-surface.

Improved SSD-assisted algorithm for surface defect detection of electromagnetic luminescence

2021 ◽  
pp. 1748006X2199538
Author(s):  
Zhenying Xu ◽  
Ziqian Wu ◽  
Wei Fan
Keyword(s):  
Defect Detection ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Detection Methods ◽  
Small Scale ◽  
Detection Accuracy ◽  
Single Shot ◽  
Surface Defect Detection ◽  
Feature Pyramid ◽  
Small Feature

Defect detection of electromagnetic luminescence (EL) cells is the core step in the production and preparation of solar cell modules to ensure conversion efficiency and long service life of batteries. However, due to the lack of feature extraction capability for small feature defects, the traditional single shot multibox detector (SSD) algorithm performs not well in EL defect detection with high accuracy. Consequently, an improved SSD algorithm with modification in feature fusion in the framework of deep learning is proposed to improve the recognition rate of EL multi-class defects. A dataset containing images with four different types of defects through rotation, denoising, and binarization is established for the EL. The proposed algorithm can greatly improve the detection accuracy of the small-scale defect with the idea of feature pyramid networks. An experimental study on the detection of the EL defects shows the effectiveness of the proposed algorithm. Moreover, a comparison study shows the proposed method outperforms other traditional detection methods, such as the SIFT, Faster R-CNN, and YOLOv3, in detecting the EL defect.

scholarly journals MTI-YOLO: A Light-Weight and Real-Time Deep Neural Network for Insulator Detection in Complex Aerial Images

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1426
Author(s):  
Chuanyang Liu ◽  
Yiquan Wu ◽  
Jingjing Liu ◽  
Jiaming Han
Keyword(s):  
Feature Detection ◽  
Feature Fusion ◽  
Memory Storage ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Composite Insulator ◽  
Running Time ◽  
Scale Feature ◽  
Multi Scale ◽  
Good Trade

Insulator detection is an essential task for the safety and reliable operation of intelligent grids. Owing to insulator images including various background interferences, most traditional image-processing methods cannot achieve good performance. Some You Only Look Once (YOLO) networks are employed to meet the requirements of actual applications for insulator detection. To achieve a good trade-off among accuracy, running time, and memory storage, this work proposes the modified YOLO-tiny for insulator (MTI-YOLO) network for insulator detection in complex aerial images. First of all, composite insulator images are collected in common scenes and the “CCIN_detection” (Chinese Composite INsulator) dataset is constructed. Secondly, to improve the detection accuracy of different sizes of insulator, multi-scale feature detection headers, a structure of multi-scale feature fusion, and the spatial pyramid pooling (SPP) model are adopted to the MTI-YOLO network. Finally, the proposed MTI-YOLO network and the compared networks are trained and tested on the “CCIN_detection” dataset. The average precision (AP) of our proposed network is 17% and 9% higher than YOLO-tiny and YOLO-v2. Compared with YOLO-tiny and YOLO-v2, the running time of the proposed network is slightly higher. Furthermore, the memory usage of the proposed network is 25.6% and 38.9% lower than YOLO-v2 and YOLO-v3, respectively. Experimental results and analysis validate that the proposed network achieves good performance in both complex backgrounds and bright illumination conditions.

scholarly journals High-Resolution SAR Image Classification Using Multi-Scale Deep Feature Fusion and Covariance Pooling Manifold Network

2021 ◽  
Vol 13 (2) ◽  
pp. 328
Author(s):  
Wenkai Liang ◽  
Yan Wu ◽  
Ming Li ◽  
Yice Cao ◽  
Xin Hu
Keyword(s):  
High Resolution ◽  
Image Classification ◽  
Feature Fusion ◽  
Representation Learning ◽  
Sar Image ◽  
Gabor Filtering ◽  
Feature Maps ◽  
Sar Images ◽  
Multi Scale ◽  
Deep Feature

The classification of high-resolution (HR) synthetic aperture radar (SAR) images is of great importance for SAR scene interpretation and application. However, the presence of intricate spatial structural patterns and complex statistical nature makes SAR image classification a challenging task, especially in the case of limited labeled SAR data. This paper proposes a novel HR SAR image classification method, using a multi-scale deep feature fusion network and covariance pooling manifold network (MFFN-CPMN). MFFN-CPMN combines the advantages of local spatial features and global statistical properties and considers the multi-feature information fusion of SAR images in representation learning. First, we propose a Gabor-filtering-based multi-scale feature fusion network (MFFN) to capture the spatial pattern and get the discriminative features of SAR images. The MFFN belongs to a deep convolutional neural network (CNN). To make full use of a large amount of unlabeled data, the weights of each layer of MFFN are optimized by unsupervised denoising dual-sparse encoder. Moreover, the feature fusion strategy in MFFN can effectively exploit the complementary information between different levels and different scales. Second, we utilize a covariance pooling manifold network to extract further the global second-order statistics of SAR images over the fusional feature maps. Finally, the obtained covariance descriptor is more distinct for various land covers. Experimental results on four HR SAR images demonstrate the effectiveness of the proposed method and achieve promising results over other related algorithms.

scholarly journals Deep Convolution Neural Networks Learned Image Classification for Early Cancer Detection Using Lightweight

2021 ◽  
Author(s):  
Seshadri Ramana K ◽  
Bala Chowdappa K ◽  
Obulesu ooruchintala ◽  
Deena Babu Mandru ◽  
kallam suresh
Keyword(s):  
Cancer Detection ◽  
Early Cancer ◽  
The Body ◽  
Early Cancer Detection ◽  
Detection Accuracy ◽  
Convolutional Network ◽  
Max Pooling ◽  
Input Layer ◽  
Deep Convolutional Network ◽  
Fully Connected

Abstract Cancer is uncontrolled cell growth in any part of the body. Early cancer detection aims to identify patients who exhibit symptoms early on in order to maximise their chances of a successful treatment. Cancer disease mortality is decreased through early detection and treatment. Numerous researchers proposed a variety of image processing and machine learning approaches for cancer detection. However, existing systems did not improve detection accuracy or efficiency. A Deep Convolutional Neural Learning Classifier Model based on the Least Mean Square Filterative Ricker Wavelet Transform (L-DCNLC) is proposed to address the aforementioned issues. The L-DCNLC Model's primary objective is to detect cancer earlier by utilising a fully connected max pooling deep convolutional network with increased accuracy and reduced time consumption. The fully connected max pooling deep convolutional network is composed of one input layer, three hidden layers, and one output layer. Initially, the input layer of the L-DCNLC Model considers the number of patient images in the database as input.

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