Robust hashing for efficient forensic analysis of image sets
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
The Flickr 1 Million dataset allows scaling experiments to large numbers of natural images.
“To achieve this, we make use of the Flickr 1 Million dataset [29], removing SHA256 hash duplicates, which allows us to scale experiments to large numbers of natural images.”
From Hamming distributions of popular perceptual hashing techniques (2023) · cites this paper as [29]
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Sorted by citations
Kumar, R., Tripathi, R., Marchang, N., Srivastava, G., Gadekallu, T. R., & Xiong, N. N. (2021) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2021.02.022 · Cited by 140 · see citing works
Hamming distributions of popular perceptual hashing techniques
McKeown, S., & Buchanan, W. J. (2023) · Forensic Science International: Digital Investigation · doi:10.1016/j.fsidi.2023.301509 · Cited by 15 · see citing works
Beyond Hamming Distance: Exploring spatial encoding in perceptual hashes
McKeown, S. (2025) · Forensic Science International: Digital Investigation · doi:10.1016/j.fsidi.2025.301878 · Cited by 3 · see citing works
- Authors
- Steinebach, M.
- Affiliations
- Fraunhofer Institute for Secure Information Technology (DE)
- Corresponding author
- Steinebach, Martin
- ORCID
- Steinebach, Martin
- Venue
- Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
- Publisher
- Springer Berlin Heidelberg
- Published
- 2012
- Pages
- 180-187
- ISSN
- 1867-8211 (ISSN-L)
- 1867-822X
- OpenAlex
- W99344506
- Cited by
- 23
- References
- 0
- Citations by year
- 2025: 2 · 2024: 7 · 2023: 3 · 2022: 3 · 2021: 2 · 2020: 1 · 2014: 4 · 2013: 1
- FWCI
- 1.14
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (publishedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Chaos-based Image/Signal Encryption
- Keywords
- robust hashing, image forensics, image sets, false alarm rate, image recognition
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.
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