Fast sequential forensic camera identification

Two sequential camera source identification methods are proposed. Sequential tests implement a log-likelihood ratio test in an incremental way, thus enabling a reliable decision with a minimal number of observations. One of our methods adapts Goljan et al.'s to sequential operation. The second, which offers better performance in terms of error probabilities and average number of test observations, is based on treating the alternative hypothesis as a doubly stochastic model. We also discuss how the standard sequential test can be corrected to account for the event of weak fingerprints. Finally, we validate the goodness of our methods with experiments.

READ FULL TEXT
research
10/08/2021

Optimal Group-Sequential Tests with Groups of Random Size

We consider sequential hypothesis testing based on observations which ar...
research
01/04/2018

Testing Optimality of Sequential Decision-Making

This paper provides a statistical method to test whether a system that p...
research
03/29/2012

Corrected Kriging update formulae for batch-sequential data assimilation

Recently, a lot of effort has been paid to the efficient computation of ...
research
06/10/2020

Deep Neural Networks for the Sequential Probability Ratio Test on Non-i.i.d. Data Series

Classifying sequential data as early as and as accurately as possible is...
research
03/08/2023

An CUSUM Test with Observation-Adjusted Control Limits in Change Detection

In this paper, we not only propose an new optimal sequential test of sum...
research
08/11/2021

Minimax and pointwise sequential changepoint detection and identification for general stochastic models

This paper considers the problem of joint change detection and identific...
research
04/19/2020

Sequential hypothesis testing in machine learning driven crude oil jump detection

In this paper we present a sequential hypothesis test for the detection ...

Please sign up or login with your details

Forgot password? Click here to reset