Introduction to the Special Issue on Security and Privacy of Avatar in Metaverse
Bibliographic record
Abstract
The Metaverse is a 3D interactive virtual community that has gained significant attention in academia, business, and industry as a potential future internet paradigm. In this space, avatars serve as key elements, acting as the primary means of human interaction. Avatars are expected to be created using real data, tailored to users' preferences, and controlled in real-time through signals from wearable devices. Avatars allow users to feel as though they are extensions of their own bodies, creating an immersive experience that blurs the line between virtual and real compared to other virtual communities. On the other hand, the avatar faces serious security and privacy problems, especially when people and the law/regulation are increasingly less tolerant of security and privacy, such as copyright, false identity detection, dataset security, authentication, and content tampering. This special issue collects 15 papers reporting the recent developments of security and privacy of avatar in metaverse. For the Avatar Copyright Protection. "A Self-Defense Copyright Protection Scheme for NFT Image Art Based on Information Embedding" addresses copyright issues related to avatars produced in the Metaverse and proposes a copyright protection scheme that not only enables tracking and verification of avatar content transactions but also validates the legality of the source and ownership of the avatar content. "Invisible Adversarial Watermarking: A Novel Security Mechanism for Enhancing Copyright Protection" addresses the potential for unauthorized access and use of image datasets used to generate avatars and proposes an image protection method that combines adversarial perturbations with invisible watermarks. This approach not only prevents illegal use of the image datasets but also enables effective tracking of data copyright. In "FaceDefend: Copyright Protection to Prevent Face Embezzle, " the authors propose a solution to the misuse problem arising from the theft of real facial image data used in avatar generation, based on defensive strategies. This approach effectively ensures copyright protection for real facial data. For the False Identity Detection for Avatars. The authors of "Audio-Visual Contrastive Pre-train for Face Forgery Detection" address the issue of potential facial privacy breaches due to the realism of avatars in virtual worlds, which can lead.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.076 | 0.034 |
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".