Fusion of Camera Model and Source Device Specific Forensic Methods for Improved Tamper Detection

TL;DR

This paper introduces a neural network-based fusion of PRNU and CNN camera model recognition methods to enhance small-scale image tamper detection, outperforming existing techniques even under compression.

Contribution

It proposes a novel fusion approach combining PRNU and CNN methods via neural networks, improving small-scale tamper localization accuracy.

Findings

Fusion outperforms individual methods under high JPEG compression.

The approach detects forgeries as small as 100x100 pixels.

Fusion surpasses state-of-the-art in small-size forgery detection.

Abstract

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.