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

Yi Zhang; Fenlin Liu; Chunfang Yang; Xiangyang Luo; Xiaofeng Song

doi:10.1117/1.JEI.24.6.063005

20 November 2015 Detection of perturbed quantization class stego images based on possible change modes

Yi Zhang, Fenlin Liu, Chunfang Yang, Xiangyang Luo, Xiaofeng Song

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 6, 063005 (November 2015). https://doi.org/10.1117/1.JEI.24.6.063005

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

Published: 20 November 2015

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KEYWORDS

Quantization

Extremely high frequency

Feature extraction

Performance modeling

Calibration

Steganography

Distortion

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