Camera-based Image Forgery Localization using Convolutional Neural Networks
This addresses image forensics for detecting manipulated images, but it is incremental as it builds on existing fingerprint techniques.
The paper tackled the problem of localizing image forgeries by introducing a CNN-based camera model fingerprint called noiseprint, which improves over the PRNU-based method.
Camera fingerprints are precious tools for a number of image forensics tasks. A well-known example is the photo response non-uniformity (PRNU) noise pattern, a powerful device fingerprint. Here, to address the image forgery localization problem, we rely on noiseprint, a recently proposed CNN-based camera model fingerprint. The CNN is trained to minimize the distance between same-model patches, and maximize the distance otherwise. As a result, the noiseprint accounts for model-related artifacts just like the PRNU accounts for device-related non-uniformities. However, unlike the PRNU, it is only mildly affected by residuals of high-level scene content. The experiments show that the proposed noiseprint-based forgery localization method improves over the PRNU-based reference.