Imaging sensor noise as digital X-ray for revealing forgeries
IH'07 Proceedings of the 9th international conference on Information hiding
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Detecting doctored JPEG images via DCT coefficient analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Exposing Digital Forgeries in Complex Lighting Environments
IEEE Transactions on Information Forensics and Security - Part 1
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Non-intrusive digital image forensics (NIDIF) aims at authenticating the validity of digital images utilizing their intrinsic characteristics when the active forensic methods, such as digital watermarking or digital signatures, fail or are not present. The NIDIF for lossy JPEG compressed images are of special importance due to its pervasively use in many applications. Recently, researchers showed that certain types of tampering manipulations can be revealed when JPEG re-compress artifacts (JRCA) is found in a suspicious JPEG image. Up to now, most existing works mainly focus on the detection of doubly JPEG compressed images without block shifting. However, they cannot identify another JRCA --- the shifted double JPEG (SD-JPEG) compression artifacts which are commonly present in composite JPEG images. In this paper, the SD-JPEG artifacts are modeled as a noisy 2-D convolutive mixing model. A symmetry verification based method and a first digit histogram based remedy method are proposed to form an integral identification framework. It can reliably detect the SD-JPEG artifacts when a critical state is not reached. The experimental results have shown the effectiveness of the proposed framework.