Adversarial Distillation via Attention Helps Enhance Accuracy and Robustness
Bibliographic record
Abstract
Lightweight neural networks are widely deployed in resource-constrained environments such as mobile devices and edge computing.However, they often struggle to achieve a reliable balance between accuracy and robustness, particularly under adversarial attacks.This limitation poses significant risks in safety-critical applications like autonomous driving and healthcare, where both high performance and reliability are essential.To address this challenge, we propose attention distillation enhancing robustness (ADER), a novel adversarial distillation framework that integrates self-attention mechanisms and a dual-teacher strategy.Unlike conventional single-teacher methods, ADER simultaneously distills knowledge from a clean teacher and an adversarially trained teacher.Furthermore, it incorporates cross-domain attention maps as auxiliary supervision to guide the student model's spatial focus during training.This design enables the student to capture both discriminative and robust features effectively.Extensive experiments on Canadian institute for advanced research (CIFAR)-10 and CIFAR-100 demonstrate that ADER consistently outperforms state-of the-art adversarial training and distillation methods.The proposed method achieves substantial improvements in both clean accuracy and adversarial robustness, highlighting its potential for secure and efficient deployment of lightweight models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".