From Model Parameters to Data Quality: Implicit Factor Evaluation of Model Extraction Attacks
Bibliographic record
Abstract
Abstract Model extraction attacks (MEAs) pose a significant threat to deep learning (DL) models, where adversaries aim to steal the decision behavior of targeted DL models. While several works have shown the ability of a surrogate model to mimic the target DL model, the underlying factors that make a DL model vulnerable to MEAs are unclear. Analyzing these underlying factors is the key to enhancing the security of DL systems. This involves exploring MEAs in diverse scenarios to understand the relationship between their success and the features of DL systems. In this paper, we evaluate the underlying factors influencing MEAs from two crucial perspectives: the model’s intrinsic parameters and the quality of the data used. For the model’s intrinsic parameters, we focus on how the batch size, learning rate, and optimizer influence the effectiveness of MEAs. Regarding data quality, we conduct an in-depth analysis of how data annotation and selection affect MEAs’ success. Our study includes analyzing variations in batch size, five learning rates, eight optimizers, the impact of varying proportions of dirty data, and the effects of subtle changes in data richness. The results of our research reveal a diverse range of susceptibilities to MEAs.
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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 | 0.017 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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 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".