![]() ![]() ![]() Our approach is also effective in detecting seam-carved forgery in JPEG images. Compared to the state-of-the-art steganalysis detectors, our 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). Our study shows that this approach has multiple promising applications in image forensics. By exploring the self-calibration under different shift recompressions, we propose calibrated neighboring joint density-based approaches with a simple feature set to distinguish steganograms and tampered images from untouched ones. In realistic detection, the untouched image and themodified version may not be obtained at the same time, and different JPEG images may have different neighboring joint density features. ![]() We analyze the neighboring joint density of the DCT coefficients and reveal the difference between the untouched image and the modified version. We have designed a method targeting the detection of both steganography and seam-carved forgery in JPEG images. Steganalysis and forgery detection in image forensics are generally investigated separately. ![]()
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