On Constructing A Better Correlation Predictor For Prnu-Based Image Forgery Localization

Localizing image forgeries is one of the key topics in multimedia forensics. Among many image forgery localization techniques, the one based on the photo-response non-uniformity (PRNU) noise has attracted substantial attention because of its capability of localizing forgeries regardless of the type of forgery. However, despite the devoted efforts to improving the performance of PRNU-based forgery localization, there remain challenges to be overcome, especially for detecting subtle forgeries in PRNU-attenuated regions due to complex image content. In this work, we investigate the feasibility and effectiveness of convolutional neural networks (CNN) in predicting PRNU correlations under complex backgrounds for more accurate forgery localization. The experimental results on 20 cameras and 200 realistic forgery images show that significant improvement in correlation prediction and forgery localization can be achieved even with a light-weight CNN model. The robustness of different correlation predictors against JPEG compression is also evaluated.