A new approach for JPEG resize and image splicing detection
MiFor '09 Proceedings of the First ACM workshop on Multimedia in forensics
An improved approach to steganalysis of JPEG images
Information Sciences: an International Journal
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
Shift recompression-based feature mining for detecting content-aware scaled forgery in JPEG images
Proceedings of the Twelfth International Workshop on Multimedia Data Mining
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As smartphones are being widely used in daily lives, the images captured by smartphones become ubiquitous and may be used for legal purposes. Accordingly, the authentication of smartphone images and the identification of post-capture manipulation are of significant interest in digital forensics. In this paper, we propose a method to determine the smartphone camera source of a particular image and operations that may have been performed on that image. We first take images using different smartphones and purposely manipulate the images, including different combinations of double JPEG compression, cropping, and rescaling. Then, we extract the marginal density in low frequency coordinates and neighboring joint density features on intra-block and inter-block as features. Finally, we employ a support vector machine to identify the smartphone source as well as to reveal the operations. Experimental results show that our method is very promising for identifying both smartphone source and manipulations. Our study also indicates that applying unsupervised clustering and supervised classification together (clustering first, followed by classification) leads to improvement in identifying smartphone sources and manipulations and thus provides a means to address the complexity issue of intentional manipulation.