Towards Augmentation Based Defense Strategies Against Adversarial Attacks
Bibliographic record
Abstract
In recent years, we have observed the growing vulnerability of deep neural networks (DNNs) to adversarial attacks, challenging the forefronts of machine learning. Adver-sarial machine learning has emerged as a crucial research area to enhance the defenses of neural networks against such attacks, especially in mission-critical vision applications. The limitations and intensive resource requirements of existing defense strategies, such as adversarial training, have spawned a search for more efficient and effective defenses in an increasingly dependent, data-driven world. We propose patch-boosting, a low-cost, data augmentation-based defense that can achieve up to a 40% percent increase in accuracy against adversarial attacks. Patch-boosting not only enhances network performance against adversarial attacks but also supports low-accuracy networks in achieving more accurate predictions. This is a promising development in adversarial machine learning, offering a practical and scalable defense mechanism against adversarial attacks.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".