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| Double JPEG compression is often used to conceal some illegal operations on the source image. Since there exist some traces when an image undergoes double JPEG compression, the detection of double JPEG compression can be used as a tool of digital forensics. In the past decade, the researchers have focused on detecting double compression with the same quality factor (QF), but cannot provide reliable results when the QF is approximately below 70. To remedy this, we propose to analyze the low-frequency components and the error image, and extract two types of features to improve the detection accuracy. Since the high-frequency components are easily lost in the JPEG compression, the low-frequency components are utilized as the reliable feature. With this in mind, our method first analyzes low-frequency discrete cosine transform (DCT) coefficients and the lost information. Then, the features are extracted to characterize the difference between the two sequential compressions. Finally, the feature set is fed to a support vector machine for classification. Experimental results on two standard image databases verify that our method could improve the detection accuracy of the low QF double compressed JPEG images with the same QF. The average classification accuracy of the different QFs (the range of QF is usually set from 75 to 20) was obtained as 88.22%. |
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| Keywords:Digital image forensic; double JPEG compression; low quality factor |
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