The Forchheim Image Database for Camera Identification in the Wild

by   Benjamin Hadwiger, et al.

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.


page 1

page 6


Noiseprint: a CNN-based camera model fingerprint

Forensic analyses of digital images rely heavily on the traces of in-cam...

On the Reliability of the PNU for Source Camera Identification Tasks

The PNU is an essential and reliable tool to perform SCI and, during the...

Image Provenance Analysis at Scale

Prior art has shown it is possible to estimate, through image processing...

Combining PRNU and noiseprint for robust and efficient device source identification

PRNU-based image processing is a key asset in digital multimedia forensi...

CNN-based fast source device identification

Source identification is an important topic in image forensics, since it...

SpoC: Spoofing Camera Fingerprints

Thanks to the fast progress in synthetic media generation, creating real...

Multi-task deep CNN model for no-reference image quality assessment on smartphone camera photos

Smartphone is the most successful consumer electronic product in today's...

Please sign up or login with your details

Forgot password? Click here to reset