Bibliographic record
Abstract
Abstract. In August 2012, the Stribog hash function was selected as the new Russian cryptographic hash standard (GOST R 34.11-2012). Stribog employs twelve rounds of an AES-based compression function operating in Miyaguchi-Preneel mode. In this paper, we investigate the preimage resistance of the Stribog hash function. Specifically, we apply a meet in the middle preimage attack on the compression function which allows us to obtain a 5-round pseudo preimage for a given compression function output with time complexity of 2 448 and memory complexity of 2 64. Additionally, we adopt a guess and determine approach to obtain a 6-round chunk separation that balances the available degrees of freedom and the guess size. The proposed chunk separation allows us to attack 6 out of 12 rounds with time and memory complexities of 2 496 and 2 112, respectively. Finally, employing 2 t multicollision, we show that preimages of the 5 and 6-round reduced hash function can be generated with time complexity of 2 481 and 2 505, respectively. The two preimage attacks have equal memory complexity of 2 256.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".