Source Camera Identification by JPEG Compression Statistics for Image Forensics

2006 ◽  
Author(s):  
Kai San Choi ◽  
Edmund Y. Lam ◽  
Kenneth K.Y. Wong
Keyword(s):  
Image Forensics ◽  
Jpeg Compression ◽  
Source Camera Identification ◽  
Camera Identification

scholarly journals Multiscale Content-Independent Feature Fusion Network for Source Camera Identification

2021 ◽  
Vol 11 (15) ◽  
pp. 6752
Author(s):  
Changhui You ◽  
Hong Zheng ◽  
Zhongyuan Guo ◽  
Tianyu Wang ◽  
Xiongbin Wu
Keyword(s):  
Adaptive Filtering ◽  
Feature Fusion ◽  
Image Data ◽  
Image Forensics ◽  
Research Progress ◽  
Image Content ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Independent Features ◽  
Copyright Authentication

In recent years, source camera identification has become a research hotspot in the field of image forensics and has received increasing attention. It has high application value in combating the spread of pornographic photos, copyright authentication of art photos, image tampering forensics, and so on. Although the existing algorithms greatly promote the research progress of source camera identification, they still cannot effectively reduce the interference of image content with image forensics. To suppress the influence of image content on source camera identification, a multiscale content-independent feature fusion network (MCIFFN) is proposed to solve the problem of source camera identification. MCIFFN is composed of three parallel branch networks. Before the image is sent to the first two branch networks, an adaptive filtering module is needed to filter the image content and extract the noise features, and then the noise features are sent to the corresponding convolutional neural networks (CNN), respectively. In order to retain the information related to the image color, this paper does not preprocess the third branch network, but directly sends the image data to CNN. Finally, the content-independent features of different scales extracted from the three branch networks are fused, and the fused features are used for image source identification. The CNN feature extraction network in MCIFFN is a shallow network embedded with a squeeze and exception (SE) structure called SE-SCINet. The experimental results show that the proposed MCIFFN is effective and robust, and the classification accuracy is improved by approximately 2% compared with the SE-SCINet network.

Digital Camera Source Identification Through JPEG Quantisation

2011 ◽  
pp. 291-313 ◽  
Author(s):  
Matthew James Sorrell
Keyword(s):  
Source Identification ◽  
Digital Camera ◽  
Jpeg Compression ◽  
Compression Algorithm ◽  
Experimental Results ◽  
Photographic Image ◽  
Source Camera Identification ◽  
Camera Identification ◽  
High Level ◽  
Camera Source Identification

We propose that the implementation of the JPEG compression algorithm represents a manufacturer and model-series specific means of identification of the source camera of a digital photographic image. Experimental results based on a database of over 5,000 photographs from 27 camera models by 10 brands shows that the choice of JPEG quantisation table, in particular, acts as an effective discriminator between model series with a high level of differentiation. Furthermore, we demonstrate that even after recompression of an image, residual artefacts of double quantisation continue to provide limited means of source camera identification, provided that certain conditions are met. Other common techniques for source camera identification are also introduced, and their strengths and weaknesses are discussed.

REVIEW ON SOURCE CAMERA IDENTIFICATION: DIGITAL IMAGE FORENSICS

2020 ◽  
Vol 7 (4) ◽  
pp. 23
Author(s):  
JAIN PRAVEE ◽  
AWASTHI MAYANK ◽  
SHANDILYA MADHU ◽  
◽  
◽  
...  
Keyword(s):  
Digital Image ◽  
Image Forensics ◽  
Digital Image Forensics ◽  
Source Camera Identification ◽  
Camera Identification

Wavelet energy feature based source camera identification for ear biometric images

2020 ◽  
Vol 130 ◽  
pp. 139-147 ◽  
Author(s):  
Debbrota Paul Chowdhury ◽  
Sambit Bakshi ◽  
Pankaj Kumar Sa ◽  
Banshidhar Majhi
Keyword(s):  
Wavelet Energy ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Feature Based ◽  
Energy Feature
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