Performance comparison of denoising filters for source camera identification

2011 ◽  
Author(s):  
A. Cortiana ◽  
V. Conotter ◽  
G. Boato ◽  
F. G. B. De Natale
Keyword(s):  
Performance Comparison ◽  
Source Camera Identification ◽  
Camera Identification

scholarly journals Efficient Source Camera Identification with Diversity-Enhanced Patch Selection and Deep Residual Prediction

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4701
Author(s):  
Yunxia Liu ◽  
Zeyu Zou ◽  
Yang Yang ◽  
Ngai-Fong Bonnie Law ◽  
Anil Anthony Bharath
Keyword(s):  
Performance Comparison ◽  
Training Data ◽  
Patch Selection ◽  
Fine Grained ◽  
Evaluation Protocol ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Prediction Module ◽  
Training Cost ◽  
The Impact

Source camera identification has long been a hot topic in the field of image forensics. Besides conventional feature engineering algorithms developed based on studying the traces left upon shooting, several deep-learning-based methods have also emerged recently. However, identification performance is susceptible to image content and is far from satisfactory for small image patches in real demanding applications. In this paper, an efficient patch-level source camera identification method is proposed based on a convolutional neural network. First, in order to obtain improved robustness with reduced training cost, representative patches are selected according to multiple criteria for enhanced diversity in training data. Second, a fine-grained multiscale deep residual prediction module is proposed to reduce the impact of scene content. Finally, a modified VGG network is proposed for source camera identification at brand, model, and instance levels. A more critical patch-level evaluation protocol is also proposed for fair performance comparison. Abundant experimental results show that the proposed method achieves better results as compared with the state-of-the-art algorithms.

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

scholarly journals Source Camera Identification Issues

2011 ◽  
Vol 3 (4) ◽  
pp. 1-15
Author(s):  
Yongjian Hu ◽  
Chang-Tsun Li ◽  
Changhui Zhou ◽  
Xufeng Lin
Keyword(s):  
Classification Accuracy ◽  
Selection Criteria ◽  
Current Source ◽  
Image Features ◽  
Forensic Identification ◽  
Forensic Investigation ◽  
Identification Schemes ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Popular Image

Statistical image features play an important role in forensic identification. Current source camera identification schemes select image features mainly based on classification accuracy and computational efficiency. For forensic investigation purposes; however, these selection criteria are not enough. Consider most real-world photos may have undergone common image processing due to various reasons, source camera classifiers must have the capability to deal with those processed photos. In this work, the authors first build a sample camera classifier using a combination of popular image features, and then reveal its deficiency. Based on the experiments, suggestions for the design of robust camera classifiers are given.

Source Camera Identification Based on Sensor Readout Noise

2010 ◽  
Vol 2 (3) ◽  
pp. 28-42 ◽  
Author(s):  
H. R. Chennamma ◽  
Lalitha Rangarajan
Keyword(s):  
Computer Software ◽  
Sensor Data ◽  
Processing Parameter ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Novel Approach ◽  
Deviation Plot ◽  
Readout Noise ◽  
The One ◽  
Imaging Sensor

A digitally developed image is a viewable image (TIFF/JPG) produced by a camera’s sensor data (raw image) using computer software tools. Such images might use different colour space, demosaicing algorithms or by different post processing parameter settings which are not the one coded in the source camera. In this regard, the most reliable method of source camera identification is linking the given image with the sensor of camera. In this paper, the authors propose a novel approach for camera identification based on sensor’s readout noise. Readout noise is an important intrinsic characteristic of a digital imaging sensor (CCD or CMOS) and it cannot be removed. This paper quantitatively measures readout noise of the sensor from an image using the mean-standard deviation plot, while in order to evaluate the performance of the proposed approach, the authors tested against the images captured at two different exposure levels. Results show datasets containing 1200 images acquired from six different cameras of three different brands. The success of proposed method is corroborated through experiments.

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