Image Complexity and Feature Extraction for Steganalysis of LSB Matching Steganography
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Image complexity and feature mining for steganalysis of least significant bit matching steganography
Information Sciences: an International Journal
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A new approach for JPEG resize and image splicing detection
MiFor '09 Proceedings of the First ACM workshop on Multimedia in forensics
Improved detection and evaluation for JPEG steganalysis
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Source camera identification using enhanced sensor pattern noise
IEEE Transactions on Information Forensics and Security
Neighboring joint density-based JPEG steganalysis
ACM Transactions on Intelligent Systems and Technology (TIST)
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A method to detect JPEG-based double compression
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
MiFor '11 Proceedings of the 3rd international ACM workshop on Multimedia in forensics and intelligence
Digital camera identification from sensor pattern noise
IEEE Transactions on Information Forensics and Security
Blind Identification of Source Cell-Phone Model
IEEE Transactions on Information Forensics and Security
Detection of Double-Compression in JPEG Images for Applications in Steganography
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Circuits and Systems for Video Technology
A novel feature selection method based on normalized mutual information
Applied Intelligence
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Digital multimedia forensics is an emerging field that has important applications in law enforcement and protection of public safety and national security. In digital imaging, JPEG is the most popular lossy compression standard and JPEG images are ubiquitous. Today's digital techniques make it easy to tamper JPEG images without leaving any visible clues. Furthermore, most image tampering involves JPEG double compression, it heightens the need for accurate analysis of JPEG double compression in image forensics.In this paper, to improve the detection of JPEG double compression, we transplant the neighboring joint density features, which were designed for JPEG steganalysis, and merge the joint density features with marginal density features in DCT domain as the detector for learning classifiers. Experimental results indicate that the proposed method improves the detection performance. We also study the relationship among compression factor, image complexity, and detection accuracy, which has not been comprehensively analyzed before. The results show that a complete evaluation of the detection performance of different algorithms should necessarily include image complexity as well as the double compression quality factor.In addition to JPEG double compression, the identification of image capture source is an interesting topic in image forensics. Mobile handsets are widely used for spontaneous photo capture because they are typically carried by their users at all times. In the imaging device market, smartphone adoption is currently exploding and megapixel smartphones pose a threat to the traditional digital cameras. While smartphone images are widely disseminated, the manipulation of images is also easily performed with various photo editing tools. Accordingly, the authentication of smartphone images and the identification of post-capture manipulation are of significant interest in digital forensics. Following the success of our previous work in JPEG double compression detection, we conducted a study to identify smartphone source and post-capture manipulation by utilizing marginal density and neighboring joint density features together. Experimental results show that our method is highly promising for identifying both smartphone source and manipulations.Finally, our study also indicates that applying unsupervised clustering and supervised classification together leads to improvement in identifying smartphone sources and manipulations and thus provides a means to address the complexity issue of the intentional post-capture manipulation on smartphone images.