Proceedings of the 3rd ACM Workshop on Information Hiding and Multimedia Security
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 3rd ACM Information Hiding and Multimedia Security Workshop -- IH&MMSec'15 in Portland, Oregon. Portland is also where the 2nd Information Hiding Workshop (IHW) was held 17 years ago. Over these years, the field has developed tremendously by the relentless effort of many scientists and researchers all over the globe, and many of their research results have been implemented into useful products. In response to our call for papers, 45 excellent papers were submitted from the United States, Canada, Europe, Asia, and Malaysia. The best 20 of these papers were accepted (44% acceptance rate) and assembled into a strong technical program. The accepted papers cover the fields of steganography and steganalysis in digital media, digital watermarking of 3D objects, covert channels, watermarking and fingerprinting codes, digital forensics, biometric and authentication, encryption, and privacy. The technical program also features two invited keynote speakers. The first presentation is about the implications of cyber warfare, and it is given by Mr. David Aucsmith, from Aucsmith Consulting LLC. The second presentation is about the privacy of the Internet of Things, and it is given by Dr. Richard Chow, from Intel Corporation. As usual, the workshop is structured into three days with the afternoon of the second day devoted to a social event. The social event is designed to promote discussions and to help establish relationships for future collaboration among participants. Also, at the end of the first day, an hour is reserved for a rump session during which the participants are encouraged to share their work in progress, discuss unpublished results, demo new products, and make relevant announcements. A great team effort put together the technical program. The Program Committee assisted by 16 external reviewers provided timely and high-quality reviews. A double-blind review process was used to ensure fairness. Each paper was carefully read and appraised by at least three reviewers. To let the Program Chairs select the best quality and relevant work, papers with conflicting reviews were discussed at length. We expect the selected papers to be of wide interest to researchers working in the field and to participants from industry and from government institutions. We thank all participants for their help in putting together this great program.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.058 | 0.024 |
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".