The DOI matches its Crossref record; 69 of the 73 references it cites were found in a registry or an index.
Depreciating Motivation and Empirical Security Analysis of Chaos-Based Image and Video Encryption
Abstract
Over the past years, an enormous variety of different chaos-based image and video encryption algorithms have been proposed and published. While any algorithm published undergoes some more or less strict experimental security analysis, many of those schemes are being broken in subsequent publications. In this paper, we show that two main motivations for preferring chaos-based image encryption over classical strong cryptographic encryption, namely computational effort and security benefits, are highly questionable. We demonstrate that several statistical tests, commonly used to assess the security of chaos-based encryption schemes, are insufficient metrics for security analysis. We do this experimentally by constructing obviously insecure encryption schemes and demonstrating that they perform well and/or pass several of these tests. In conclusion, these tests can only give a necessary, but by no means a sufficient condition for security. As a consequence of this paper, several security analyses in related work are questionable; further, methodologies for the security assessment for chaos based encryption schemes need to be entirely reconsidered.
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In the citing paper
“Considering the quantitative test results of the four special schemes on the metrics, it is concluded that the metrics are far ineffective for security analysis”
From When an attacker meets a cipher-image in 2018: A year in review (2019) · cites this paper as [116]
In the citing paper
“In [116], four obviously insecure encryption schemes are subtly constructed to question credibility of the security assessment metrics used in the field of chaotic cryptography widely”
From When an attacker meets a cipher-image in 2018: A year in review (2019) · cites this paper as [116]
In the citing paper
“Among them, the most striking work is depreciating motivation of most chaos-based encryption schemes in [116], refuting any superiority of chaos-based encryption schemes than several variants of AES in terms of computational effort and security performance”
From When an attacker meets a cipher-image in 2018: A year in review (2019) · cites this paper as [116]
In the citing paper
“As shown in [116,117], the security assessment metrics used in most chaos-based image encryption schemes are unconvincing.”
From When an attacker meets a cipher-image in 2018: A year in review (2019) · cites this paper as [116]
In the citing paper
“The security test metrics are widely misused.”
From When an attacker meets a cipher-image in 2018: A year in review (2019) · cites this paper as [116]
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Metadata
- Authors
- Preishuber, M., Hutter, T., Katzenbeisser, S., & Uhl, A.
- Affiliations
- University of Salzburg (AT)
- Technische Universität Darmstadt (DE)
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2018-09
- Volume
- 13
- Issue
- 9
- Pages
- 2137-2150
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W2790225553
- Cited by
- 256
- References
- 73
- Citations by year
- 2026: 9 · 2025: 23 · 2024: 23 · 2023: 29 · 2022: 48 · 2021: 34 · 2020: 51 · 2019: 32 · 2018: 7
- FWCI
- 15.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Information Forensics and Security (publishedVersion)
- Funders
- Austrian Science Fund (P27776)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Chaos-based Image/Signal Encryption
- Physical Sciences › Computer Science › Artificial Intelligence › Computer Science and Engineering
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptographic Implementations and Security
- Keywords
- chaos-based cryptography, image encryption, video encryption, randomness testing, security analysis, cryptanalysis, security metrics, computational effort
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 ›
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