Fast Johnson-Lindenstrauss Transform for robust and secure image hashing
Why this work is in the frame
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Bibliographic record
Abstract
Dimension reduction based techniques, such as singular value decomposition (SVD) and non-negative matrix factorization (NMF), have been proved to provide excellent performance for robust and secure image hashing by retaining the essential features of the original image matrix while preventing intentional attacks. In this paper, we introduce a recently proposed low-distortion, dimension reduction technique, referred as Fast Johnson-Lindenstrauss Transform (FJLT), and propose the use of FJLT for image hashing. FJLT shares the low-distortion characteristics of a random projection but requires a much lower complexity. These two desirable properties make it suitable for image hashing. Our experiment results show that the proposed FJLT-based hash yields good robustness under a wide range of attacks. Furthermore, the influence of secret key on the proposed hashing algorithm is evaluated by receiver operating characteristics (ROC) graph, revealing the efficiency of the proposed approach.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it