Abstract

Sensor pattern noise (SPN) is an inherent fingerprint of imaging devices, which provides an effective way for source camera identification (SCI). Although SPNs extracted from large image blocks usually yield high identification accuracy, their high dimensionality would incur a high computational cost in the matching stage, consequently hindering many applications that require efficient camera matchings. In this work, we employ and evaluate the concept of principal component analysis (PCA) de-noising in SCI tasks. Based on this concept, we present a framework that formulates a compact SPN representation. To enhance the de-noising effect, we introduce a training set construction procedure that minimizes the impact of various interfering artifacts, which is especially useful in some challenging cases, e.g., when only textured reference images are available. To further boost the SCI performance, a novel approach based on linear discriminant analysis (LDA) is adopted to extract more discriminant SPN features. To evaluate our methods, extensive experiments are conducted on the Dresden image database. The results indicate that the proposed framework can serve as an effective post-processing procedure, which not only boosts the performance, but also greatly reduces the computational cost in the matching phase.

Highlights

  • Nowadays, the use of digital images or videos as evidence in the fight against physical crime and cybercrime is a norm, which makes multimedia forensics crucial

  • To alleviate the common limitation of the afore-mentioned Sensor pattern noise (SPN) compression methods [13,14,15,16,17], in our previous work [19,20], we presented a feature extraction algorithm based on the concept of principal component analysis (PCA) de-noising [21,22], and promising results were achieved on a small dataset

  • We introduced and evaluated the concept of PCA de-noising in the source camera identification (SCI) task

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Summary

Introduction

The use of digital images or videos as evidence in the fight against physical crime and cybercrime is a norm, which makes multimedia forensics crucial. To alleviate the common limitation (i.e., reduced accuracy) of the afore-mentioned SPN compression methods [13,14,15,16,17], in our previous work [19,20], we presented a feature extraction algorithm based on the concept of PCA de-noising [21,22], and promising results were achieved on a small dataset This method is based on the assumption that the training set is well representative of the population so that an effective SPN feature extractor can be learned. In this manuscript, we use bold upper-case letters to represent matrices, and bold lower-case letters to denote vectors

Background
SPN extraction
Reference SPN estimation
SPN matching
Proposed SPN feature extraction and enhancement
Training set construction
Training sample enhancement
SPN feature extraction through PCA
SPN feature enhancement through LDA
Source camera identification
Experiments
Experimental setup
Parameter settings and discussions
Method
Distributions of intra-class and inter-class correlations
Performance comparison – accuracy
Performance comparison – compactness
Performance comparison – computational complexity
References vectors
Findings
Conclusion
Full Text
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