Impact of Model Architecture Against Adversarial Example's Effectivity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this study is to gain an understanding of the impact of model architecture on the efficacy of adversarial examples against machine learning systems implemented in self-driving applications. Prior research shows how to create and train against adversarial examples in many use cases; however, there is no definite understanding of how a machine learning model’s architecture affects the efficacy of adversarial examples. Data was collected through an experimental setting involving end-to-end self-driving models trained through behavioral cloning. Three model types were tested based on popular frameworks for machine learning algorithms dealing with images. Results showed a statistically significant difference in the impact of adversarial examples between these models. This means that certain model types and architectures are more susceptible to attacks. Therefore, the conclusion can be made that model architecture does impact the efficacy of adversarial examples; however, this is potentially limited to closed-loop, end-to-end systems in which algorithms make the entire decision. Future research should investigate what specific structure within models causes increased susceptibility to adversarial attacks.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it