A context adaptive predictor of sensor pattern noise for camera source identification

Sensor pattern noise (SPN) is a noise-like spread-spectrum signal inherently cast onto every digital image by each imaging device and has been recognised as a reliable device fingerprint for camera source identification (CSI) and image origin verification. It can be estimated as the noise residual between the image content and its denoised version. However, the SPN extracted from a single image can be contaminated largely by image scene because image edge noise is usually much stronger than the SPN. So the identification performance is heavily dependent upon the purity of the estimated SPN, especially for small size images because they have less and weaker SPN. Although there are some existing works dedicated to improving the performance of source camera identification, an effective method to eliminate the contamination of image scene and extract an accurate SPN is currently lacking. In this paper, we will propose an edge adaptive SPN predictor based on context adaptive interpolation (PCAI) to exclude the contamination of image scene. Different from most of the existing methods extracting SPN from wavelet high frequency coefficients, we extract SPN directly from the spatial domain with a pixel-wise adaptive Wiener filter, based on the assumption that the SPN is a white signal. Extensive experiments show that our proposed PCAI method achieves the best receiver operating characteristic (ROC) performance among all of the state-of-the-art CSI schemes on different sizes of images, and has the best performance in resisting JPEG compression (e.g. with a quality factor of 90%) simultaneously.