Cascade Adversarial Attack Search
Bibliographic record
Abstract
Adversarial attack is a technique that introduces small and imperceptible perturbations into input data to force deep neural networks to make incorrect predictions. This not only helps assess model robustness and security but also reveals potential vulnerabilities, providing a foundation for optimizing defense mechanisms. However, single-design attack models often struggle to cope with complex and evolving defense strategies. In addition, traditional methods that rely on manual parameter tuning are inadequate for capturing internal model information in black-box scenarios, making it difficult to maintain efficiency and transferability across diverse target models and data distributions. To address this, this paper proposes a Cascade Adversarial Attack Search approach based on multi-objective optimization strategies, named CAAS. Specifically, this method constructs a comprehensive search space encompassing various attack algorithms, models, and their hyperparameter combinations. It employs a cascade strategy to sequentially apply multiple attack techniques, aiming to improve transfer attack success rates while reducing attack costs. Experimental results demonstrate that when tested on ten randomly selected models, CAAS not only significantly improves attack success rates, but also effectively controls attack costs, showcasing its superior performance in the field of adversarial attacks.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".