Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.23919/EUSIPCO.2018.8553581 | European Signal Processing Conference |
Keywords | DocType | Volume |
Image forensics,PRNU,convolutional neural networks | Conference | abs/1808.09714 |
ISSN | Citations | PageRank |
2076-1465 | 3 | 0.37 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Davide Cozzolino | 1 | 358 | 19.37 |
| Luisa Verdoliva | 2 | 971 | 57.12 |