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Detection of JPEG double compression and identification of smartphone image source and post-capture manipulation

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Abstract

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.

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Acknowledgements

This project was supported in part by Award No. 2010-DN-BX-K223 awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice. The opinions, findings, and conclusions or recommendations expressed in this publication/program/exhibition are those of the authors and do not necessarily reflect those of the Department of Justice. Partial support from the Research and Sponsored Program at Sam Houston State University under 2011 and 2012 Enhancement Research Grants and from the Institute for Complex Additive Systems Analysis at New Mexico Tech is greatly appreciated. We are also very grateful to Xiaodong Li for his assistance in our study.

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Authors and Affiliations

  1. Department of Computer Science, Sam Houston State University, Huntsville, TX, 77341, USA

    Qingzhong Liu, Peter A. Cooper, Lei Chen & Hyuk Cho

  2. Department of Epidemiology and Biostatistics, School of Public Health, Indiana University, Bloomington, IN, 47405-7109, USA

    Zhongxue Chen

  3. Department of Mathematics and Computer Science, South Dakota School of Mines and Technology, Rapid City, SD, 57701, USA

    Mengyu Qiao

  4. School of Electronic Information Engineering, Tianjin University, Tianjin, 300072, China

    Yuting Su

  5. Department of Geological Sciences and Engineering, Missouri University of Science and Technology, Rolla, MO, 65409, USA

    Mingzhen Wei

  6. Department of Computer Science and Institute for Complex Additive Systems Analysis, New Mexico Tech, Socorro, NM, 87801, USA

    Andrew H. Sung

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  1. Qingzhong Liu
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Correspondence to Qingzhong Liu.

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Liu, Q., Cooper, P.A., Chen, L. et al. Detection of JPEG double compression and identification of smartphone image source and post-capture manipulation. Appl Intell 39, 705–726 (2013). https://doi.org/10.1007/s10489-013-0430-z

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