Abstract: In literature, there are very few discussions on the change of performance of source camera classification algorithms when test images are subjected to minor image processing. Using Support Vector Machines (SVM), this paper analyzed the performance and robustness of source camera classification algorithms. It compared the detection accuracy for unprocessed images with that for processed images, and investigated the robustness of different types of image features. Since pattern classification-based algorithms often need to reduce the number of image features for computational efficiency, this paper also discussed the performance of camera classification algorithms using the image feature subsets. The impact of using these subsets on the robustness of camera classification algorithms was explored as well.

Key words: digital image forensics, source camera classification, Support Vector Machine (SVM), feature selection, robustness

摘要： 现有文献中的源相机分类算法很少讨论测试图像在受到轻微图像处理后算法性能的变化。利用支持向量机，对源相机分类算法的性能和鲁棒性进行了分析，比较了测试图像遭受处理前后分类算法的检测准确率，并研究了图像特征的鲁棒性。由于基于模式分类的算法通常需要精简特征个数以提高执行效率，因此，还讨论了精简模式下相机分类算法的性能以及特征选择对辨识算法鲁棒性的影响。

关键词: 数字图像取证, 源相机分类, 支持向量机, 特征选择, 鲁棒性