A PRNU-based counter-forensic method to manipulate smartphone image source identification techniques

2017 ◽  
Vol 76 ◽  
pp. 418-427 ◽  
Author(s):  
Luis Javier García Villalba ◽  
Ana Lucila Sandoval Orozco ◽  
Jocelin Rosales Corripio ◽  
Julio Hernandez-Castro
Keyword(s):  
Source Identification ◽  
Image Source ◽  
Identification Techniques

Anti‐noise image source identification

2018 ◽  
Vol 31 (19) ◽  
Author(s):  
Yuying Liu ◽  
Yonggang Huang ◽  
Jiao Zhang ◽  
Hualei Shen
Keyword(s):  
Source Identification ◽  
Image Source ◽  
Noise Image

Noise Source Identification Techniques Comparison

2008 ◽  
Author(s):  
Georges Vretos Glyniadakis ◽  
Marlon Casagrande Rodrigues ◽  
Mauro Miguel Pecula ◽  
Alexandre Barcelos de Souza
Keyword(s):  
Source Identification ◽  
Noise Source ◽  
Noise Source Identification ◽  
Identification Techniques

Sediment source identification: a review and a case study in some Canadian streamsThis paper is one of a selection of papers in this Special Issue in honour of Professor M. Selim Yalin (1925–2007).

2009 ◽  
Vol 36 (10) ◽  
pp. 1622-1633 ◽  
Author(s):  
Bommanna G. Krishnappan ◽  
Patricia A. Chambers ◽  
Glenn Benoy ◽  
Joseph Culp
Keyword(s):  
Source Identification ◽  
State Of The Art ◽  
Control Strategies ◽  
Agricultural Watersheds ◽  
Sediment Source ◽  
Sediment Control ◽  
Adverse Impacts ◽  
Identification Techniques ◽  
Selection Of

The state-of-the-art of sediment source identification is reviewed in this paper. Sediment “fingerprinting” techniques using different “fingerprint” properties were examined. With these techniques, it is possible to identify potential sources of sediment transported in river systems. Such knowledge is useful for implementing sediment control strategies to limit sediment production from upland areas in a watershed as well as for developing guidelines for land use practices to minimize adverse impacts on surface and ground water resources in agricultural watersheds. Examples of sediment source identification techniques that were carried out in agricultural watersheds in different parts of the world were also included in the present review.

Noisy Smoothing Image Source Identification

2017 ◽  
pp. 135-147 ◽  
Author(s):  
Yuying Liu ◽  
Yonggang Huang ◽  
Jun Zhang ◽  
Xu Liu ◽  
Hualei Shen
Keyword(s):  
Source Identification ◽  
Image Source

scholarly journals A Jungle Computing approach to common image source identification in large collections of images

2018 ◽  
Vol 27 ◽  
pp. 3-16 ◽  
Author(s):  
B. van Werkhoven ◽  
P. Hijma ◽  
C.J.H. Jacobs ◽  
J. Maassen ◽  
Z.J.M.H. Geradts ◽  
...  
Keyword(s):  
Source Identification ◽  
Image Source ◽  
Computing Approach

Comparison of dust source identification techniques over land in the Middle East region using MODIS data

2012 ◽  
Vol 38 (5) ◽  
pp. 586-599 ◽  
Author(s):  
Neamat Karimi ◽  
Ali Moridnejad ◽  
Saeed Golian ◽  
Jamal Mohammad Vali Samani ◽  
Danesh Karimi ◽  
...  
Keyword(s):  
Middle East ◽  
Source Identification ◽  
Dust Source ◽  
Modis Data ◽  
East Region ◽  
Identification Techniques ◽  
Middle East Region

scholarly journals Virtual Sample Generation and Ensemble Learning Based Image Source Identification With Small Training Samples

2021 ◽  
Vol 13 (3) ◽  
pp. 34-46
Author(s):  
Shiqi Wu ◽  
Bo Wang ◽  
Jianxiang Zhao ◽  
Mengnan Zhao ◽  
Kun Zhong ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Source Identification ◽  
Diffusion Theory ◽  
Training Sample ◽  
Virtual Sample ◽  
Image Source ◽  
Camera Identification ◽  
Average Accuracy ◽  
Training Samples ◽  
Sample Set

Nowadays, source camera identification, which aims to identify the source camera of images, is quite important in the field of forensics. There is a problem that cannot be ignored that the existing methods are unreliable and even out of work in the case of the small training sample. To solve this problem, a virtual sample generation-based method is proposed in this paper, combined with the ensemble learning. In this paper, after constructing sub-sets of LBP features, the authors generate a virtual sample-based on the mega-trend-diffusion (MTD) method, which calculates the diffusion range of samples according to the trend diffusion theory, and then randomly generates virtual sample according to uniform distribution within this range. In the aspect of the classifier, an ensemble learning scheme is proposed to train multiple SVM-based classifiers to improve the accuracy of image source identification. The experimental results demonstrate that the proposed method achieves higher average accuracy than the state-of-the-art, which uses a small number of samples as the training sample set.

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