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Detection of perturbed quantization class stego images based on possible change modes

[+] Author Affiliations
Yi Zhang, Fenlin Liu, Chunfang Yang, Xiaofeng Song

State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, Henan 450002, China

Zhengzhou Science and Technology Institute, No. 62 of Science Road, P.O. Box 405, Zhengzhou, Henan Province 450002, China

Xiangyang Luo

State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, Henan 450002, China

Science and Technology on Information Assurance Laboratory, Beijing 100072, China

J. Electron. Imaging. 24(6), 063005 (Nov 20, 2015). doi:10.1117/1.JEI.24.6.063005
History: Received June 23, 2015; Accepted October 6, 2015
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Abstract.  To improve the detection performance for perturbed quantization (PQ) class [PQ, energy-adaptive PQ (PQe), and texture-adaptive PQ (PQt)] stego images, a detection method based on possible change modes is proposed. First, by using the relationship between the changeable coefficients used for carrying secret messages and the second quantization steps, the modes having even second quantization steps are identified as possible change modes. Second, by referencing the existing features, the modified features that can accurately capture the embedding changes based on possible change modes are extracted. Next, feature sensitivity analyses based on the modifications performed before and after the embedding are carried out. These analyses show that the modified features are more sensitive to the original features. Experimental results indicate that detection performance of the modified features is better than that of the corresponding original features for three typical feature models [Cartesian calibrated PEVny (ccPEV), Cartesian calibrated co-occurrence matrix features (CF), and JPEG rich model (JRM)], and the integrated feature consisting of enhanced histogram features (EHF) and the modified JRM outperforms two current state-of-the-art feature models, namely, phase aware projection model (PHARM) and Gabor rich model (GRM).

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Citation

Yi Zhang ; Fenlin Liu ; Chunfang Yang ; Xiangyang Luo and Xiaofeng Song
"Detection of perturbed quantization class stego images based on possible change modes", J. Electron. Imaging. 24(6), 063005 (Nov 20, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.6.063005


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