Information Forensics and Security: A quarter-century-long journey
Open accessAbstract
Information forensics and security (IFS) is an active R&D area whose goal is to ensure that people use devices, data, and intellectual properties for authorized purposes and to facilitate the gathering of solid evidence to hold perpetrators accountable. For over a quarter century, since the 1990s, the IFS research area has grown tremendously to address the societal needs of the digital information era. The IEEE Signal Processing Society (SPS) has emerged as an important hub and leader in this area, and this article celebrates some landmark technical contributions. In particular, we highlight the major technological advances by the research community in some selected focus areas in the field during the past 25 years and present future trends.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Perceptual hashing generates similar binary hashes for perceptually similar contents and different hashes for different contents.
“Perceptual hashing, also known as robust hashing, is a content fingerprinting technique that generates similar binary hashes for perceptually similar contents and different hashes for different contents [1].”
From Robustness of Practical Perceptual Hashing Algorithms to Hash-Evasion and Hash-Inversion Attacks (2024) · cites this paper as [1]
Cites
Secure spread spectrum watermarking for multimedia
Cox, I. J., Kilian, J., Leighton, F. T., & Shamoon, T. (1997) · IEEE Transactions on Image Processing · doi:10.1109/83.650120 · Cited by 5,299 · see citing works
Rich Models for Steganalysis of Digital Images
Fridrich, J., & Kodovsky, J. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2190402 · Cited by 2,144 · see citing works
Self-citationInformation 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 · see citing works
Self-citationOverview on Selective Encryption of Image and Video: Challenges and Perspectives
Massoudi, A., Lefebvre, F., De Vleeschouwer, C., Macq, B., & Quisquater, J.-J. (2008) · EURASIP Journal on Information Security · doi:10.1155/2008/179290 · Cited by 172 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Deepfake Media Forensics: Status and Future Challenges
Amerini, I., Barni, M., Battiato, S., Bestagini, P., Boato, G., Bruni, V., Caldelli, R., De Natale, F., De Nicola, R., Guarnera, L., et al. (2025) · Journal of Imaging · doi:10.3390/jimaging11030073 · Cited by 113 · see citing works
Self-citationRobustness of Practical Perceptual Hashing Algorithms to Hash-Evasion and Hash-Inversion Attacks
Madden, J., Bhavsar, M., Dorje, L., & Li, X. (2024) · arXiv · doi:10.48550/arxiv.2406.00918
“Perceptual hashing, also known as robust hashing, is a content fingerprinting technique that generates similar binary hashes for perceptually similar contents and different hashes for different contents [1].”
- Authors
- Barni, M., Campisi, P., Delp, E. J., Doërr, G., Fridrich, J., Memon, N., Pérez-González, F., Rocha, A., Verdoliva, L., & Wu, M.
- Affiliations
- University of Siena (IT)
- Roma Tre University (IT)
- Purdue University West Lafayette (US)
- Synamedia, Rennes, France
- Binghamton University (US)
- New York University (US)
- University of Vigo, Vigo, Spain
- Universidade Estadual de Campinas (UNICAMP) (BR)
- University of Naples Federico II (IT)
- University of Maryland, College Park (US)
- ORCID
- Venue
- IEEE Signal Processing Magazine
- Published
- 2023-07
- Volume
- 40
- Issue
- 5
- Pages
- 67-79
- ISSN
- 1053-5888 (ISSN-L)
- 1558-0792
- OpenAlex
- W4384937769
- Cited by
- 28
- References
- 4
- Citations by year
- 2026: 3 · 2025: 13 · 2024: 8 · 2023: 4
- FWCI
- 2.54
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- IEEE Signal Processing Magazine (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Iris (Roma Tre University) (submittedVersion)
- Use Siena air (University of Siena) (submittedVersion)
- Investigo Institutional repository of UVigo (Universidade de Vigo) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Spanish Ministry for Science and Innovation (MCIN/AEI/10.13039/501100011033)
- European Regional Development (ED431C 2021/47)
- National Science Foundation (2028119)
- 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 › Information Systems › Digital and Cyber Forensics
- Keywords
- information forensics, digital forensics, digital security, digital evidence, intellectual property protection, digital watermarking, digital signatures, image forensics, digital video forensics, digital fingerprinting, digital steganography
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