开放环境下自适应聚类优化包络的相机来源取证

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

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k-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.

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 k-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.