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Filtered Image Forensics Based on Frequency Domain Features

Image filtering has been widely used in image tampering for denoising and anti-forensics. The detection of image filtering has attracted much attention in recent years, and several methods have been done. However, there are still some challenging works to do. In this paper, a novel method is proposed for two kinds of image filtering detection. The forensics algorithm is based on DCT-LQPAT (local quadruple pattern based on discrete cosine transform) feature in frequency domain. Firstly, the spatial image is converted into the frequency domain by block DCT transform. Secondly, the DCT-LQPAT feature is calculated by local quadruple pattern in the frequency domain. Finally, the extracted DCT-LQPAT feature set is fed into a SVM (support vector machine) classifier for training and testing. A series of experiments are carried out for filtered image forensics. The experimental results show that this algorithm has a better performance compared with other classic methods even in detecting filtering in high compressed images.