Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 451

The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.

Efficient Dense-Field Copy–Move Forgery Detection

Cozzolino, Davide, Poggi, Giovanni, Verdoliva, Luisa·2015·IEEE Transactions on Information Forensics and Security
DOI 10.1109/tifs.2015.2455334
Open this paper in CoChat — free account

Abstract

We propose a new algorithm for the accurate detection and localization of copy-move forgeries, based on rotation-invariant features computed densely on the image. Dense-field techniques proposed in the literature guarantee a superior performance with respect to their keypoint-based counterparts, at the price of a much higher processing time, mostly due to the feature matching phase. To overcome this limitation, we resort here to a fast approximate nearest-neighbor search algorithm, PatchMatch, especially suited for the computation of dense fields over images. We adapt the matching algorithm to deal efficiently with invariant features, so as to achieve higher robustness with respect to rotations and scale changes. Moreover, leveraging on the smoothness of the output field, we implement a simplified and reliable postprocessing procedure. The experimental analysis, conducted on databases available online, proves the proposed technique to be at least as accurate, generally more robust, and typically much faster than the state-of-the-art dense-field references.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/tifs.2015.2455334

Cites

41 of 41 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 41 references

Research works printed but not linked: 10

  1. jing, 2010, Image tamper detection algorithm based on Radon and Fourier–Mellin transform, Proc IEEE Int Conf Inf Theory Inf Secur, 212
  2. 10.1109/LSP.2011.2163507 · doi:10.1109/lsp.2011.2163507
  3. 10.1007/978-3-642-16693-8_11 · doi:10.1007/978-3-642-16693-8_11
  4. 10.1016/j.forsciint.2010.08.001 · doi:10.1016/j.forsciint.2010.08.001
  5. 10.1016/j.forsciint.2011.07.015 · doi:10.1016/j.forsciint.2011.07.015
  6. 10.1016/j.forsciint.2013.09.013 · DOI
  7. 10.1109/TIP.2006.888346 · doi:10.1109/tip.2006.888346
  8. 10.1364/JOSAA.3.000771 · doi:10.1364/josaa.3.000771
  9. 10.1109/TPAMI.1983.4767371 · doi:10.1109/tpami.1983.4767371
  10. 10.1109/34.735809 · doi:10.1109/34.735809

Cited by

See all 451

Metadata

Authors
Cozzolino, D., Poggi, G., & Verdoliva, L.
Venue
IEEE Transactions on Information Forensics and Security
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2015-11
Volume
10
Issue
11
Pages
2284-2297
ISSN
  • 1556-6013 (ISSN-L)
  • 1556-6021
DOI
10.1109/tifs.2015.2455334
OpenAlex
W1570958768
Cited by
451
References
41
FWCI
23.97
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Efficient Dense-Field Copy–Move Forgery Detection. https://app.cochat.ai/papers/10.1109/tifs.2015.2455334
The paper
Cozzolino, D., Poggi, G., & Verdoliva, L. (2015). Efficient Dense-Field Copy–Move Forgery Detection. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/tifs.2015.2455334

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.