Identification of the source camera of images based on convolutional neural network

Huang, Na; He, Jingsha; Zhu, Nafei; Xuan, Xinggang; Liu, Gongzheng; Chang, Cheng-Yue

doi:10.1016/j.diin.2018.08.001

Cited by 33 publications

(29 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After processed by the softmax function, the output vector will be converted into the form of the probability distribution. Its mathematical expression can be expressed as follows [ 51 , 52 ]:

where

is the output vector of the output layer.

are the element values of the output vector of m category.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Zhu

Wang

et al. 2021

Sensors

View full text Add to dashboard Cite

Hydraulic piston pump is the heart of hydraulic transmission system. On account of the limitations of traditional fault diagnosis in the dependence on expert experience knowledge and the extraction of fault features, it is of great meaning to explore the intelligent diagnosis methods of hydraulic piston pump. Motivated by deep learning theory, a novel intelligent fault diagnosis method for hydraulic piston pump is proposed via combining wavelet analysis with improved convolutional neural network (CNN). Compared with the classic AlexNet, the proposed method decreases the number of parameters and computational complexity by means of modifying the structure of network. The constructed model fully integrates the ability of wavelet analysis in feature extraction and the ability of CNN in deep learning. The proposed method is employed to extract the fault features from the measured vibration signals of the piston pump and realize the fault classification. The fault data are mainly from five different health states: central spring failure, sliding slipper wear, swash plate wear, loose slipper, and normal state, respectively. The results show that the proposed method can extract the characteristics of the vibration signals of the piston pump in multiple states, and effectively realize intelligent fault recognition. To further demonstrate the recognition property of the proposed model, different CNN models are used for comparisons, involving standard LeNet-5, improved 2D LeNet-5, and standard AlexNet. Compared with the models for contrastive analysis, the proposed method has the highest recognition accuracy, and the proposed model is more robust.

show abstract

“…After processed by the softmax function, the output vector will be converted into the form of the probability distribution. Its mathematical expression can be expressed as follows [ 51 , 52 ]:

where

is the output vector of the output layer.

are the element values of the output vector of m category.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Zhu

Wang

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…A series of recent studies have presented classification and counting models related to individuals, cars and other objects [32], [34], [35], [37], [48]- [51] which provide useful neural network model references for industrial component identification and counting research. There are already several well-known deep-learning Convolutional Neural Network (CNN) applications, such as AlexNet [47], [52], International Journal of Machine Learning and Computing, Vol.…”

Section: A Classical Neural Network Modelsmentioning

confidence: 99%

Warehouse Management Models Using Artificial Intelligence Technology with Application at Receiving Stage – A Review

Yang¹,

Li²,

Rasul³

2021

IJMLC

View full text Add to dashboard Cite

This paper reviewed recent literature on inventory management technologies and Artificial Intelligence (AI) applications. The classical Artificial Neural Network (ANN) models and computer vision technology applications for object classification were reviewed in particularly. The challenges of AI technologies in industrial warehouse management, particularly the ANN for solving object classification and counting are discussed. Some researchers reported the use of face recognition, moving vehicle classification and counting, which are easy to recognise objects on the floor or the ground. Other researchers explored the object counting technologies which are used to identify the visible objects on the ground or in images. Although several studies focused on industrial component identification and counting problems, a study on the warehouse receiving stage remains a blank canvas. This paper reviews and analyses current industrial warehouse management developments around AI applications in this field, which may provide a reference for future researchers and end-users for the best modelling approach to this specific problem at the warehouse receiving stage.

show abstract

“…Identification of images that might have a common source can also be helpful in these investigations. The developments that have been started in the period of the previous review have not been stopped and have lead to a number of new methods and software packages [ [51] , [52] , [53] , [55] , [56] , [57] , [58] , [59] , [60] , [61] , [62] , [63] , [64] , [65] , [66] , [67] , [68] , [69] , [70] , [71] , [72] , [73] , [74] , [75] , [76] , [b] , [77] , [a] , [b] , [78] , [a] , [79] , [80] , [81] , [82] , [83] , [84] , [85] , [86] , [87] , [88] , [89] , [9] ]. The most used method is based on the estimation of a specific type of fixed pattern noise in an image that is caused by PRNU - Photo Response Non Uniformity .…”

Section: Camera Identification Of Images and Videomentioning

confidence: 99%

“…Other sources of fixed pattern noise [ 52 , 66 , 78 , 85 ] that have been investigated are based on detection of image artefacts from differences in image processing in the camera chips. Also deep learning is combined with PRNU detection [ 56 , 71 ].…”

Section: Camera Identification Of Images and Videomentioning

confidence: 99%

Interpol review of imaging and video 2016–2019

Geradts

Filius

Ruifrok

2020

Forensic Science International: Synergy

View full text Add to dashboard Cite

show abstract

Identification of the source camera of images based on convolutional neural network

Cited by 33 publications

References 11 publications

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Warehouse Management Models Using Artificial Intelligence Technology with Application at Receiving Stage – A Review

Interpol review of imaging and video 2016–2019

Contact Info

Product

Resources

About