scholarly journals Implementation of the likelihood ratio framework for camera identification based on sensor noise patterns

2011 ◽  
Vol 10 (2) ◽  
pp. 149-159 ◽  
Author(s):  
W. van Houten ◽  
I. Alberink ◽  
Z. Geradts
Keyword(s):  
Likelihood Ratio ◽  
Sensor Noise ◽  
Camera Identification

scholarly journals ANTI-FORENSICS: COUNTERING SENSOR NOISE CAMERA IDENTIFICATION

2020 ◽  
pp. 45-51
Author(s):  
Nikhith Suvarna
Keyword(s):  
Data Hiding ◽  
Forensic Analysis ◽  
Sensor Noise ◽  
Analysis Techniques ◽  
Camera Identification ◽  
Source Data ◽  
Highly Effective ◽  
Evidence Source ◽  
Noise Signature ◽  
Tools And Techniques

In simple terms, Anti-Forensics can be told as the techniques used to counter forensic analysis done by forensic investigators. This paper mainly focuses on some of the most used anti-forensics techniques along with the challenges the forensics investigator faces. There are many tools and techniques available that when used properly can be highly effective against the forensic analysis techniques. Various tools assist you against various anti-forensics techniques like Elimination of evidence source, Data hiding, and Trail obfuscation. These techniques are used mainly to make the investigation consume more time and money. Sensor Noise Camera Identification is a way to link a photo with the camera the photo was taken from using a noise signature that is unique for every camera. KEYWORDS: Anti-Forensics (AF), Forensic Analysis, Anti-Forensic Techniques, Sensor Noise Camera Identification

scholarly journals Sensor noise camera identification: countering counter-forensics

2010 ◽  
Author(s):  
Miroslav Goljan ◽  
Jessica Fridrich ◽  
Mo Chen
Keyword(s):  
Sensor Noise ◽  
Camera Identification

Camera identification based on sensor noise pattern: a practical procedure for open scenarios

2013 ◽  
Author(s):  
Hermeson Costa ◽  
Ronaldo Zampolo ◽  
Adalbery Castro ◽  
Eurípedes Santos ◽  
Diego Carmo
Keyword(s):  
Sensor Noise ◽  
Noise Pattern ◽  
Practical Procedure ◽  
Camera Identification

A likelihood ratio test for detecting patterns of disease-marker association

1997 ◽  
Vol 61 (4) ◽  
pp. 335-350 ◽  
Author(s):  
A. P. MORRIS ◽  
J. C. WHITTAKER ◽  
R. N. CURNOW
Keyword(s):  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Ratio Test ◽  
Disease Marker

Predicting Fetal Chromosome Anomalies in the First Trimester Using Pregnancy Associated Plasma Protein-A: A Comparison of Statistical Methods

1993 ◽  
Vol 32 (02) ◽  
pp. 175-179 ◽  
Author(s):  
B. Brambati ◽  
T. Chard ◽  
J. G. Grudzinskas ◽  
M. C. M. Macintosh
Keyword(s):  
Logistic Regression ◽  
General Population ◽  
Likelihood Ratio ◽  
False Positive ◽  
False Positive Rate ◽  
Ratio Method ◽  
Detection Rates ◽  
Gaussian Distributions ◽  
Positive Rate ◽  
Likelihood Ratio Method

Abstract:The analysis of the clinical efficiency of a biochemical parameter in the prediction of chromosome anomalies is described, using a database of 475 cases including 30 abnormalities. A comparison was made of two different approaches to the statistical analysis: the use of Gaussian frequency distributions and likelihood ratios, and logistic regression. Both methods computed that for a 5% false-positive rate approximately 60% of anomalies are detected on the basis of maternal age and serum PAPP-A. The logistic regression analysis is appropriate where the outcome variable (chromosome anomaly) is binary and the detection rates refer to the original data only. The likelihood ratio method is used to predict the outcome in the general population. The latter method depends on the data or some transformation of the data fitting a known frequency distribution (Gaussian in this case). The precision of the predicted detection rates is limited by the small sample of abnormals (30 cases). Varying the means and standard deviations (to the limits of their 95% confidence intervals) of the fitted log Gaussian distributions resulted in a detection rate varying between 42% and 79% for a 5% false-positive rate. Thus, although the likelihood ratio method is potentially the better method in determining the usefulness of a test in the general population, larger numbers of abnormal cases are required to stabilise the means and standard deviations of the fitted log Gaussian distributions.

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