The Forchheim Image Database for Camera Identification in the Wild

TL;DR

The paper introduces the Forchheim Image Database (FODB), a comprehensive dataset designed to evaluate camera identification algorithms under realistic social media post-processing conditions, addressing gaps in existing datasets.

Contribution

FODB is the first dataset to simultaneously separate scene content from forensic traces and include social media recompression effects, enabling more rigorous algorithm benchmarking.

Findings

EfficientNet outperforms dedicated forensic CNNs on both clean and compressed images.

Performance improves with data augmentation even on unknown post-processing.

FODB's design imposes strict conditions for evaluating forensic algorithms.

Abstract

Image provenance can represent crucial knowledge in criminal investigation and journalistic fact checking. In the last two decades, numerous algorithms have been proposed for obtaining information on the source camera and distribution history of an image. For a fair ranking of these techniques, it is important to rigorously assess their performance on practically relevant test cases. To this end, a number of datasets have been proposed. However, we argue that there is a gap in existing databases: to our knowledge, there is currently no dataset that simultaneously satisfies two goals, namely a) to cleanly separate scene content and forensic traces, and b) to support realistic post-processing like social media recompression. In this work, we propose the Forchheim Image Database (FODB) to close this gap. It consists of more than 23,000 images of 143 scenes by 27 smartphone cameras, and it allows to cleanly separate image content from forensic artifacts. Each image is provided in 6 different qualities: the original camera-native version, and five copies from social networks. We demonstrate the usefulness of FODB in an evaluation of methods for camera identification. We report three findings. First, the recently proposed general-purpose EfficientNet remarkably outperforms several dedicated forensic CNNs both on clean and compressed images. Second, classifiers obtain a performance boost even on unknown post-processing after augmentation by artificial degradations. Third, FODB's clean separation of scene content and forensic traces imposes important, rigorous boundary conditions for algorithm benchmarking.