CA-UAP: Content-Agnostic Universal Adversarial Perturbation for Enhanced Generalization
Bibliographic record
Abstract
Deep Neural Networks (DNNs) have been shown vulnerable to universal adversarial perturbation (UAP), which are imperceptible and capable of fooling the target model for most samples. Existing universal attack methods mainly focus on aggregating the gradient obtained from global image features to directly optimize (noise-based) or indirectly generate (generator-based) UAP. However, such methods do not yet consider improving the generalization of UAP from the perspective of making the perturbation irrelevant to image content. We note that minimizing self-similarity is helpful to make the UAP irrelevant to the image content. Therefore, we propose a novel Content-Agnostic UAP (CA-UAP), which combines global image features and local patch features to optimize UAP. Specifically, we introduce a self-similarity loss that encourages minimizing the similarity between adversarial perturbed global images and their randomly cropped local regions, making the UAP agnostic to image content and consequently enhancing UAP generalization. Extensive experiments on the ILSVRC 2012 dataset demonstrate that our proposed method outperforms existing methods in both untargeted and targeted attacks, e.g., improving the average fooling rate from 79.12% (achieved by the state-of-the-art method) to 82.25% in targeted 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".