Guided Erasable Adversarial Attack (GEAA) Toward Shared Data Protection
Bibliographic record
Abstract
In recent years, there has been increasing interest in studying the adversarial attack, which poses potential risks to deep learning applications and has stimulated numerous researches, e.g. improving the robustness of deep neural networks. In this work, we propose a novel double-stream architecture – Guided Erasable Adversarial Attack (GEAA) – for protecting high-quality labeled data with high commercial values under data-sharing scenarios. GEAA contains three phases, the double-stream adversarial attack, denoising reconstruction, and watermark extraction. Specifically, the double-stream adversarial attack injects erasable perturbations into the training data to avoid database abuse. The denoising reconstruction rebuilds the traceable denoising data from adversarial examples. The watermark extraction recovers identity information from the denoised data for copyright protection. Additionally, we introduce the annealing optimization strategy to balance these phases and a boundary constraint to degrade the availability of adversarial examples. Through extensive experiments, we demonstrate the effectiveness of the proposed framework in data protection. The Pytorch® implementations of GEAA can be downloaded from an open-source Github project https://github.com/Dlut-lab-zmn/ GEAA-for-data-protection.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".