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Improved Approaches With Calibrated Neighboring Joint Density to Steganalysis and Seam-Carved Forgery Detection in JPEG Images

NCJ Number
Date Published
January 2015
0 pages
Steganalysis and forgery detection in image forensics are generally investigated separately. The project reported in this article designed a method that targets the detection of both steganography and seam-carved forgery in JPEG images.
The project analyzed the neighboring joint density of the DCT coefficients and revealed the difference between the untouched image and the modified version. In realistic detection, the untouched image and the modified version may not be obtained at the same time, and different JPEG images may have different neighboring joint density features. By exploring the self-calibration under different shift recompressions, the authors propose calibrated neighboring joint density-based approaches with a simple feature set to distinguish steganograms and tampered images from untouched ones. The study shows that this approach has multiple promising applications in image forensics. Compared to the state-of-the-art steganalysis detectors, the proposed approach delivers better or comparable detection performances with a much smaller feature set while detecting several JPEG-based steganographic systems including DCT-embedding-based adaptive steganography and Yet Another Steganographic Scheme (YASS). The proposed approach is also effective in detecting seam-carved forgery in JPEG images. By integrating calibrated neighboring density with spatial domain rich models that were originally designed for steganalysis, the hybrid approach obtains the best detection accuracy in discriminating seam-carved forgery from an untouched image. The study also offers a promising means of exploring steganalysis and forgery detection together. (Publisher abstract modified)

Date Published: January 1, 2015