Detecting Image Forgeries using Geometric Cues

Detecting Image Forgeries using Geometric Cues

Lin Wu (Tianjin University, China) and Yang Wang (Tianjin University, China)
Copyright: © 2011 |Pages: 21
DOI: 10.4018/978-1-60960-024-2.ch012
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This chapter presents a framework for detecting fake regions by using various methods including watermarking technique and blind approaches. In particular, we describe current categories on blind approaches which can be divided into five: pixel-based techniques, format-based techniques, camera-based techniques, physically-based techniques and geometric-based techniques. Then we take a second look on the geometric-based techniques and further categorize them in detail. In the following section, the state-of-the-art methods involved in the geometric technique are elaborated.
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Today's digital technology has begun to erode our trust on the integrity of the visual imagery since image editing software can generate highly photorealistic images (Farid, 2009). Doctored photographs are appearing with a growing frequency and sophistication in tabloid magazines, mainstream media outlets, political campaigns, photo hoaxes, evidences in a courtroom, insurance claims, and cases involving scientific fraud (Farid, 2009). With the rapid advancement in image editing software, photorealistic images will become increasingly easier to be generated and it becomes difficult for people to differentiate them from photographic images (Lyu & Farid, 2005). If we are to have any hope that photographs can hold the unique stature of being a definitive recording of events, we must develop technologies that can detect the tampered images. Therefore, authenticating the integrity of digital image's content has become particularly important when images are used as critical evidence in journalism and security surveillance applications.

Over the past several years, the field of digital forensics has emerged to authenticate digital images by enforcing several authentication methods. The presence or absence of the watermark in interpolated images captured by the camera can be employed to establish the authenticity of digital color images. Digital watermarking (I.J. Cox & M.L. Miller & J.A. Bloom, 2002; H. Liu & J. Rao & X. Yao, 2008) has been proposed as a means to authenticate an image. However, a watermarking must be inserted at the time of recording, which would limit this approach to specially equipped digital cameras having no capabilities to add a watermarking at the time of image capture. Furthermore, the watermarking would be destroyed if the image is compressed and the ruin of watermark would make the method failed.

Passive (nonintrusive) image forensics is regarded as the future direction. In contrast to the active methods, blind approaches need no prior information that is used in the absence of any digital watermarking or signature. Blind approaches can be roughly grouped into five categories (Farid, 2009):

  • 1.

    pixel-based techniques that analyze pixel-level correlations arising from tampering. Efficient algorithms based on pixels have been proposed to detect cloned (B. Mahdian & S. Saic, 2007; A. Popescu & H. Farid, 2004; J. Fridrich & D. Soukal & J. Lukas, 2003), re-sampled (A. C. Popescu & H. Farid, 2005), spliced (T. T. Ng & S. F. Chang, 2004; T. T. Ng & S. F. Chang & Q. Sun, 2004; W. Chen, & Y. Shi, & W. Su, 2007) images.Statistical properties (H. Farid & S. Lyu, 2003; S. Bayram, & N. Memon, & M. Ramkumar, & B. Sankur, 2004) in natural images are also utilized;

  • 2.

    format-based techniques detect tampering in lossy image compression: unique properties of lossy compression such as JPEG can be exploited for forensic analysis (H. Farid, 2008; J. Lukas & J. Fridrich, 2003; T. Pevny & J. Fridrich, 2008).

  • 3.

    camera-based techniques exploit artifacts introduced by the camera lens, sensor or on-chip post-processing (J. Lukas, & J. Fridrich & M. Goljan, 2005; A. Swaminathan & M. Wu & K. J. Ray Liu, 2008). Models of color filter array (A. C. Popescu & H. Farid, 2005; S. Bayram & H. T. Sencar & N. Memon, 2005), camera response (Y. F. Hsu & S. F. Chang, 2007; Z. Lin & R. Wang & X. Tang & H.Y. Shum, 2005) and sensor noise (H. Gou & A. Swaminathan & M. Wu, 2007; M. Chen & J. Fridrich &M. Goljan & J. Lukas, 2008; J. Lukas, & J. Fridrich & M. Goljan, 2005) are estimated to infer the source digital cameras and reveal digitally altered images. Other work such as (A. Swaminathan & M. Wu & K. J. Ray Liu, 2008) trace the entire in-camera and post-camera processing operations to identify the source digital cameras and reveal digitally altered images using the intrinsic traces.

  • 4.

    physically-based techniques model and detect anomalies using physical rules. For example, three dimensional interaction between physical objects, light, and the camera can be used as evidence of tampering (M.K. Johnson & H. Farid, 2005; M. K. Johnson & H. Farid, 2007).

  • 5.

    geometric-based techniques make use of geometric constraints that are preserved or recovered from perspective views (M. K. Johnson & H. Farid, 2006; M. K. Johnson, 2007; W. Wang & H. Farid, 2008; W. Zhang & X. Cao & Z. Feng & J. Zhang & P. Wang, 2009; W. Zhang & X. Cao & J. Zhang & J.Zhu.&P. Wang, 2009).

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