Envelope Optimization Based on Adaptive Clustering for Open-Set Camera Model Identification

WANG Bo; WANG Yue; WANG Wei; HOU Jia-yao

doi:10.12263/DZXB.20211428

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Envelope Optimization Based on Adaptive Clustering for Open-Set Camera Model Identification

PAPERS | 更新时间：2025-12-08

- Envelope Optimization Based on Adaptive Clustering for Open-Set Camera Model Identification
- ACTA ELECTRONICA SINICA Vol. 50, Issue 8, Pages: 1840-1850(2022)
- 作者机构：
  
  1.大连理工大学信息与通信工程学院，辽宁大连 116024
  2.中国科学院自动化所智能感知与计算研究中心，北京 100190
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20211428
  CLC： TP391;
- Received：19 October 2021，
  
  Revised：2022-02-09，
  
  Published：25 August 2022
- 稿件说明：
移动端阅览
王波,王悦,王伟等.开放环境下自适应聚类优化包络的相机来源取证[J].电子学报,2022,50(08):1840-1850.

WANG Bo,WANG Yue,WANG Wei,et al.Envelope Optimization Based on Adaptive Clustering for Open-Set Camera Model Identification[J].ACTA ELECTRONICA SINICA,2022,50(08):1840-1850.
王波,王悦,王伟等.开放环境下自适应聚类优化包络的相机来源取证[J].电子学报,2022,50(08):1840-1850. DOI： 10.12263/DZXB.20211428.

WANG Bo,WANG Yue,WANG Wei,et al.Envelope Optimization Based on Adaptive Clustering for Open-Set Camera Model Identification[J].ACTA ELECTRONICA SINICA,2022,50(08):1840-1850. DOI： 10.12263/DZXB.20211428.

摘要

针对相机来源取证中的开放环境问题，本文提出一种自适应聚类优化包络的相机来源取证方法，解决了现有方法在训练相机模型数量少的恶劣情况下检测精度低的问题.首先，通过手肘法得到每一类相机数据的聚类个数，并以该聚类数为参照进行

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1.43933344

2.28600001

-means聚类；然后将得到的相机模型子类数据分别进行支持向量数据描述以刻画其子包络，并根据所属相机模型类别将子包络合成一个更具细节特征的特征包络；最后通过判决法则将来自未知相机模型的图像排除，并将判断为已知来源的图像分类溯源，进而实现开放环境下的相机来源鉴别.实验结果表明，在Dresden和SOCRatES两个公开数据集上，本文提出的算法具有更优的鲁棒性和扩展性，与已有方法相比，在KACC，UACC和OACC三个评估指标和时间复杂度上均表现出更优越的性能.

Abstract

In this paper

an envelope optimization based on adaptive clustering for open-set camera model identification is proposed for the open-set problem of source camera identification

which solved the problem of low detection accuracy of the existing methods in the bad situation with few known camera models. Firstly

the clustering number of each type of camera data is obtained by the elbow method

and

-means clustering is performed with this clustering number as the reference. Then the sub-class data of the camera model are described by the technique of support vector data description

respectively to describe its hypersphere sub-envelope

and the sub-envelope is synthesized into a new hypersphere envelope with more detailed features according to the class of the camera model. Finally

the images from unknown camera models are excluded by the decision rule

and the images from known sources are classified and traceable to achieve source camera identification in the open-set. Experimental results on the two public datasets Dresden and SOCRatES show that the method proposed in this paper has better robustness and scalability. Compared with the existing methods

the three evaluation indicators of KACC

UACC

and OACC and time complexity are superior.

关键词

Keywords

references

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