Biometric-Like Approach for Verifying Artworks Authenticity
Bibliographic record
Abstract
The artwork market is plenty of unauthorized reproduction of original products. One of the most varies filed is the counterfeiting of Authenticity Certificate related to paints, lithography, sculptures, etc., with the aim to create an illegal market of reproduced copies. To resolve this problematic, it is possible change the current paper certificate, related to a single artwork, with a digital version, which will contain some specific information, related to the artwork itself. In this paper, starting with the well-known advantages given by the biometry paradigm in human authentication, we propose a method able to distinguish the single “non-living” objects. In other words, we propose an approach that, by using the random inimitably characteristics, is able to uniquely identify artworks such as painting, lithographs, sculptures, etc. In this way it could be possible creating a secure digital certificate of authenticity (digital COA). Due to the high density information available in modern acquisition media, it is possible using a Speckle Metrology approach. During verification phase, the same area has to be acquired, to extract embedded verification data. It is possible to secure this data using a private key, necessary for accepting the digital signature. The presence of possible geometrical distortions between image present in the certificate and acquired during the verification phase, it is necessary applying geometrical corrections based on affine transformation, before executing the correlation methodologies, used in speckle metrology.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".