PRNU-based Image Forgery Localization With Convolutional Neural Network

The device fingerprint, photo-response non-uniformity (PRNU), has attracted great interest in image tampering detection and localization. The classical PRNU-based tampering detection generally depends on the correlation analysis, with the normalized correlation and hand-crafted predictor. The predictor detects unreliable regions and determines them as forgery, regardless of other information. However, the operation is arbitrary and the auxiliary information provided by such a predictor is hard to achieve satisfactory results. Motivated by this, we propose a lightweight forgery detection strategy, where a localization result is directly predicted by a supervised convolutional neural network (CNN). For the first time, CNN is introduced to compute the correlation coefficient in PRNU-based forgery detection. We perform an extensive evaluation in both pixel-level and image-level experiments, and the results show that the proposed method achieves significant performance gains.