.png){kind=icon}
.png){kind=icon}
Return
Blind PRNU-Based Image Clustering for Source Identification
.png){kind=icon}
PRE
AI
DOI：10.1109/TIFS.2017.2701335.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We address the problem of clustering a set of images, according to their source device, in the absence of any prior information. Image similarity is computed based on noise residuals, regarded as single-image estimates of the camera's photo-response non-uniformity (PRNU) pattern. First, residuals are grouped by correlation clustering, and several alternative data partitions are computed as a function of a running decision boundary. Then, these partitions are processed jointly to extract a single, more reliable, consensus clustering and, with it, more reliable PRNU estimates. Finally, both clustering and PRNU estimates are progressively refined by merging pairs of the same-PRNU clusters, selected on the basis of a maximum-likelihood ratio statistic. Extensive experiments prove the proposed method to outperform the current state of the art both on pristine images and compressed images downloaded from social networks. A remarkable feature of the method is that it does not require the user to set any parameter, nor to provide a training set to estimate them. Moreover, through a suitable choice of basic tools, and efficient implementation, complexity remains always quite limited.
Keywords:
PRNU
camera identification
source identification
image forensics
consensus clustering
correlation clustering
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8
Papers:
5.2K
Citations:
2.3W