Abstract
Sensor pattern noise (SPN) extraction is a critical stage of the sensor based source camera identification (SCI). However, the quality of the extracted SPN with the traditional discrete wavelet transform (DWT) based method is poor around strong edges and along with the image border. To fill this gap, we propose a dual tree complex wavelet transform (DTCWT) based method to extract the SPN from a given image, which achieves better performance in the area around strong edges. Furthermore, symmetric boundary extension instead of the periodized boundary extension is used for enhancing the quality of SPN along with the image border. Extensive experimental results on both synthetic noisy images and real-world photographs clearly demonstrate the superior SCI performance of the proposed method over state-of-the-arts. Moreover, the proposed method also shows potential in the application of image tampering localization.
Highlights
Source camera identification (SCI) establishes the relationship between a questioned image and its source device, which is important when this image serves as serious evidence
LIMITATIONS OF THE BASELINE METHOD As a good start point, we examine the limitations of the discrete wavelet transform (DWT) based SPN extraction, aliasing artifacts and boundary effect, with a clean image corrupted with synthetic sensor pattern noise
PERFORMANCE COMPARISON Figure 7 shows the histogram of the PCE values with the DWT based method [6], Anisotropic method [10], BM3D method [9], CAGIF method [12], and the proposed dual tree complex wavelet transform (DTCWT) based method with periodized boundary extension and
Summary
Source camera identification (SCI) establishes the relationship between a questioned image and its source device, which is important when this image serves as serious evidence. Among the available SCI approaches [1]–[5], the approach based on sensor pattern noise (SPN) is the most promising one and has been extensively studied in the last decade [5]–[17]. The SPN, which is caused by photoresponse non-uniformity (PRNU) due to inhomogeneity of silicon wafers [18], differs from camera to camera, even of the same model from the same manufacture. The sensor based SCI method can identify individual cameras of even the same model. The SPN W is estimated from a given image I as the difference between itself and its denoised version
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