Cryptanalysis of Round-Reduced Fantomas, Robin and iSCREAM
Bibliographic record
Abstract
In this work, we focus on LS-design ciphers Fantomas, Robin, and iSCREAM. LS-designs are a family of bitslice ciphers aimed at efficient masked implementations against side-channel analysis. We have analyzed Fantomas and Robin with a technique that previously has not been applied to both algorithms or linear cryptanalysis. The idea behind linear cryptanalysis is to build a linear characteristic that describes the relation between plaintext and ciphertext bits. Such a relationship should hold with probability 0.5 (bias is zero) for a secure cipher. Therefore, we try to find a linear characteristic between plaintext and ciphertext where bias is not equal to zero. This non-random behavior of cipher could be converted to some key-recovery attack. For Fantomas and Robin, we find 5 and 7-round linear characteristics. Using these characteristics, we attack both the ciphers with reduced rounds and recover the key for the same number of rounds. We also apply linear cryptanalysis to the famous CAESAR candidate iSCREAM and the closely related LS-design Robin. For iScream, we apply linear cryptanalysis to the round-reduced cipher and find a 7-round best linear characteristics. Based on those linear characteristics we extend the path in the related-key scenario for a higher number of rounds.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".