Adversarial Machine Learning: Attacks and Defenses in Deep Neural Networks
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
Deep neural networks (DNNs) have achieved remarkable success across a wide range of applications, from image recognition to natural language processing. However, their vulnerability to adversarial attacks—deliberate perturbations crafted to mislead models—has raised significant concerns regarding their deployment in security-critical systems. This paper provides a comprehensive overview of adversarial machine learning, focusing on both attack strategies and defense mechanisms. We categorize and analyze various adversarial attack methods, including gradient-based, optimization-based, and transfer-based approaches. Additionally, we explore state-of-the-art defenses designed to improve model robustness, such as adversarial training, defensive distillation, and input transformation techniques. By examining the interplay between adversaries and defenders, we highlight the ongoing arms race in adversarial machine learning and discuss open challenges and future research directions for building more secure and trustworthy DNN-based systems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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