Robust scanner identification based on noise features

A large portion of digital image data available today is acquired using digital cameras or scanners. While camerasallow 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 scanner 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.