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.
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