Robust scanner identification based on noise features

A large portion of digital image data available today is acquired using digital cameras or scanners. While cameras allow digital reproduction of natural scenes, scanners are often used to capture hardcopy art in more controlled scenarios. This paper proposes a new technique for non-intrusive seamier model identification, which can be further extended to perform tampering detection on scanned images. Using only scanned image samples that contain arbitrary content,, we construct a robust scanner identifier to determine the brand/model of the scanner used to capture each scanned image. The proposed scanner identifier is based on statistical features of scanning noise. We first analyze scanning noise from several angles, including through image de-noising, wavelet analysis, and neighborhood prediction, and then obtain statistical features from each characterization. Experimental results demonstrate that the proposed method can effectively identify the correct scanner brands/models with high accuracy.