Information Hiding: 6th International Workshop, IH 2004, Toronto, Canada, May 23-25, 2004, Revised Selected Papers
Bibliographic record
Abstract
Session 1 - Digital Media Watermarking Session Chair: Lisa Marvel (University of Delaware).- An Implementation of, and Attacks on, Zero-Knowledge Watermarking.- On the Possibility of Non-invertible Watermarking Schemes.- Reversing Global and Local Geometrical Distortions in Image Watermarking.- On Achievable Regions of Public Multiple-Access Gaussian Watermarking Systems.- Fixed-Distortion Orthogonal Dirty Paper Coding for Perceptual Still Image Watermarking.- Session 2 - Steganalysis Session Chair: Mauro Barni (University of Siena).- Feature-Based Steganalysis for JPEG Images and Its Implications for Future Design of Steganographic Schemes.- Exploiting Preserved Statistics for Steganalysis.- Improved Detection of LSB Steganography in Grayscale Images.- An Improved Sample Pairs Method for Detection of LSB Embedding.- Session 3 - Forensic Applications Session Chair: Scott Craver (Princeton University).- Statistical Tools for Digital Forensics.- Relative Generic Computational Forensic Techniques.- Session 4 - Steganography Session Chair: Andreas Westfeld (Dresden University of Technology).- Syntax and Semantics-Preserving Application-Layer Protocol Steganography.- A Method of Linguistic Steganography Based on Collocationally-Verified Synonymy.- Session 5 - Software Watermarking Session Chair: John McHugh (SEI/CERT).- Graph Theoretic Software Watermarks: Implementation, Analysis, and Attacks.- Threading Software Watermarks.- Soft IP Protection: Watermarking HDL Codes.- Session 6 - Security and Privacy Session Chair: Ross Anderson (University of Cambridge).- An Asymmetric Security Mechanism for Navigation Signals.- Analysis of COT-based Fingerprinting Schemes: New Approach to Design Practical and Secure Fingerprinting Scheme.- Empirical and Theoretical Evaluation of Active Probing Attacks and Their Countermeasures.- Optimization and Evaluation of Randomized c-Secure CRT Code Defined on Polynomial Ring.- Session 7 - Anonymity Session Chair: Andreas Pfitzmann (Dresden University of Technology).- Statistical Disclosure or Intersection Attacks on Anonymity Systems.- Reasoning About the Anonymity Provided by Pool Mixes That Generate Dummy Traffic.- The Hitting Set Attack on Anonymity Protocols.- Session 8 - Data Hiding in Unusual Content Session Chair: Christian Collberg (University of Arizona).- Information Hiding in Finite State Machine.- Covert Channels for Collusion in Online Computer Games.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.114 | 0.043 |
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".