Fusion of Camera Model and Source Device Specific Forensic Methods for Improved Tamper Detection
This work addresses the challenge of localizing small-size image forgeries for forensic applications, representing an incremental improvement over existing methods.
The paper tackles the problem of detecting small-scale image tampering by fusing PRNU-based camera recognition and CNN-based camera model recognition methods via a neural network, achieving improved performance over state-of-the-art methods for forgeries as small as 100x100 pixels, even under high JPEG compression.
PRNU based camera recognition method is widely studied in the image forensic literature. In recent years, CNN based camera model recognition methods have been developed. These two methods also provide solutions to tamper localization problem. In this paper, we propose their combination via a Neural Network to achieve better small-scale tamper detection performance. According to the results, the fusion method performs better than underlying methods even under high JPEG compression. For forgeries as small as 100$\times$100 pixel size, the proposed method outperforms the state-of-the-art, which validates the usefulness of fusion for localization of small-size image forgeries. We believe the proposed approach is feasible for any tamper-detection pipeline using the PRNU based methodology.