Enhancing adversarial transferability with local transformation
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
Robust deep learning models have demonstrated significant applicability in real-world scenarios. The utilization of adversarial attacks plays a crucial role in assessing the robustness of these models. Among such attacks, transfer-based attacks, which leverage white-box models to generate adversarial examples, have garnered considerable attention. These transfer-based attacks have demonstrated remarkable efficiency, particularly under the black-box setting. Notably, existing transfer attacks often exploit input transformations to amplify their effectiveness. However, prevailing input transformation-based methods typically modify input images indiscriminately, overlooking regional disparities. To bolster the transferability of adversarial examples, we propose the Local Transformation Attack (LTA) based on forward class activation maps. Specifically, we first obtain future examples through accumulated momentum and compute forward class activation maps. Subsequently, we utilize these maps to identify crucial areas and apply pixel scaling for transformation. Finally, we update the adversarial examples by using the average gradient of the transformed image. Extensive experiments convincingly demonstrate the effectiveness of our proposed LTA. Compared to the current state-of-the-art attack approaches, LTA achieves an increase of 7.9% in black-box attack performance. Particularly, in the case of ensemble attacks, our method achieved an average attack success rate of 98.3%.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it