Compressed Binary Image Hashes Based on Semisupervised Spectral Embedding
Bibliographic record
Abstract
Conventional image hashing maps invariant features of each digital image into a unique, compact, robust, and secure signature, which can be used as an index for fast content identification and copyright protection. This paper addresses an important issue of compressing the real-valued image hashes into short binary signatures, which can support fast image identification using Hamming distance metrics. The proposed binary image hashing approach presents a fundamental departure from existing methods: Prior information from virtual image distortions and attacks is explored the first time in image hash generation. More specifically, the proposed scheme takes advantages of the extended hash feature space from virtual distortions and attacks and generates the binary signature for each image based on spectral embedding. Since the objective function to learn the embedding is designed to both preserve local similarity between distorted copies of the same image and to distinguish visually distinct images, the generated binary image hash is more robust compared with the one using conventional quantization-based compression approaches. Further, the proposed method can be generalized to combine different types of image hashes to generate a fixed-length binary signature. Our experimental results demonstrate that the proposed binary image hash by combining different real-valued image hashes is more robust against various distortions and it is computationally efficient for image similarity comparison using Hamming metrics.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".