The DOI matches its Crossref record.
Hiding Traces of Resampling in Digital Images
Abstract
Resampling detection has become a standard tool for forensic analyses of digital images. This paper presents new variants of image transformation operations which are undetectable by resampling detectors based on periodic variations in the residual signal of local linear predictors in the spatial domain. The effectiveness of the proposed method is supported with evidence from experiments on a large image database for various parameter settings. We benchmark detectability as well as the resulting image quality against conventional linear and bicubic interpolation and interpolation with a sinc kernel. These early findings on ldquocounter-forensicrdquo techniques put into question the reliability of known forensic tools against smart counterfeiters in general, and might serve as benchmarks and motivation for the development of much improved forensic techniques.
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.
Integrity signals
Registry
Cites
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.
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
Cited by
Media Forensics and DeepFakes: An Overview
Verdoliva, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3002101 · Cited by 979
Exposing DeepFake Videos By Detecting Face Warping Artifacts
Li, Y., & Lyu, S. (2018) · arXiv · doi:10.48550/arxiv.1811.00656 · Cited by 785
Median Filtering Forensics Based on Convolutional Neural Networks
Chen, J., Kang, X., Liu, Y., & Wang, Z. J. (2015) · IEEE Signal Processing Letters · doi:10.1109/lsp.2015.2438008 · Cited by 425
Information Forensics: An Overview of the First Decade
Stamm, M. C., Wu, M., & Liu, K. J. R. (2013) · IEEE Access · doi:10.1109/access.2013.2260814 · Cited by 394
On detection of median filtering in digital images
Kirchner, M., & Fridrich, J. (2010) · SPIE Proceedings · doi:10.1117/12.839100 · Cited by 278
Self-citation
Metadata
- Authors
- Kirchner, M., & Bohme, R.
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2008-12
- Volume
- 3
- Issue
- 4
- Pages
- 582-592
- ISSN
- 1556-6013 (ISSN-L)
- OpenAlex
- W2159244236
- Cited by
- 208
- References
- 0
- FWCI
- 11.46
- Type
- journal article
- Language
- en
- Open access
- closed
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: Hiding Traces of Resampling in Digital Images. https://app.cochat.ai/papers/10.1109/tifs.2008.2008214
- The paper
- Kirchner, M., & Bohme, R. (2008). Hiding Traces of Resampling in Digital Images. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/tifs.2008.2008214
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.
