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

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

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

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.

Conditions for Effective Detection and Identification of Primary Quantization of Re-Quantized JPEG Images

2011 ◽  
pp. 224-238
Author(s):  
Matthew J. Sorell
Keyword(s):  
Digital Image ◽  
Source Identification ◽  
Small Range ◽  
Jpeg Image ◽  
Image Source ◽  
Jpeg Images ◽  
Potential Source ◽  
Detection And Identification ◽  
Secondary Stage

The choice of Quantization Table in a JPEG image has previously been shown to be an effective discriminator of digital image cameras by manufacturer and model series. When a photograph is recompressed for transmission or storage, however, the image undergoes a secondary stage of quantization. It is possible, however, to identify primary quantization artifacts in the image coefficients, provided that certain image and quantization conditions are met. This chapter explores the conditions under which primary quantization coefficients can be identified, and hence can be used image source identification. Forensic applications include matching a small range of potential source cameras to an image.

Exploring Feature Coupling and Model Coupling for Image Source Identification

2018 ◽  
Vol 13 (12) ◽  
pp. 3108-3121 ◽  
Author(s):  
Yonggang Huang ◽  
Longbing Cao ◽  
Jun Zhang ◽  
Lei Pan ◽  
Yuying Liu
Keyword(s):  
Source Identification ◽  
Model Coupling ◽  
Image Source
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