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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".