The nature of statistical learning theory
The nature of statistical learning theory
Image and Video Compression for Multimedia Engineering
Image and Video Compression for Multimedia Engineering
Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines
IH '02 Revised Papers from the 5th International Workshop on Information Hiding
A Markov process based approach to effective attacking JPEG steganography
IH'06 Proceedings of the 8th international conference on Information hiding
IH'04 Proceedings of the 6th international conference on Information Hiding
IWDW'06 Proceedings of the 5th international conference on Digital Watermarking
A Survey of Passive Image Tampering Detection
IWDW '09 Proceedings of the 8th International Workshop on Digital Watermarking
Tampered region localization of digital color images based on JPEG compression noise
IWDW'10 Proceedings of the 9th international conference on Digital watermarking
Revealing digital fakery using multiresolution decomposition and higher order statistics
Engineering Applications of Artificial Intelligence
Digital image splicing detection based on approximate run length
Pattern Recognition Letters
Improved run length based detection of digital image splicing
IWDW'11 Proceedings of the 10th international conference on Digital-Forensics and Watermarking
Countering universal image tampering detection with histogram restoration
IWDW'12 Proceedings of the 11th international conference on Digital Forensics and Watermaking
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Aiming at detecting secret information hidden in a given image using steganographic tools, steganalysis has been of interest for years. In particular, universal steganalysis, not limited to attacking a specific steganographic tool, is of extensive interests due to its practicality. Recently, splicing detection, another important area in digital forensics has attracted increasing attention. Is there any relationship between steganalysis and splicing detection? Is it possible to apply universal steganalysis methodologies to splicing detection? In this paper, we address these intact and yet interesting questions. Our analysis and experiments have demonstrated that, on the one hand, steganography and splicing have different goals and strategies, hence, generally causing different statistical artifacts on images. However, on the other hand, both of them make the touched (stego or spliced) image different from the corresponding original (natural) image. Therefore, natural image model based on a set of carefully selected statistical features under the machine learning framework can be used for steganalysis and splicing detection. It is shown in this paper that some successful universal steganalytic schemes can make promising progress in splicing detection if applied properly. A more advanced natural image model developed from these state-of-the-art steganalysis methods is thereafter presented. Furthermore, a concrete implementation of the proposed model is applied to the Columbia Image Splicing Detection Evaluation Dataset, which has achieved an accuracy of 92%, indicating a significant advancement in splicing detection.