Bibliographic record
Abstract
In recent years, more researchers pay their attention to the security of artificial intelligence. The backdoor attack is one of the threats and has a powerful, stealthy attack ability. There exist a growing trend towards the triggers is that the triggers become dynamic and global. In this paper, we propose a novel global backdoor trigger generated by procedural noise. Our backdoor triggers are much stealthy and straightforward to implement compared with most triggers. There are three types of procedural noise, and we evaluate the attack ability for the triggers with them on the different classification datasets, including CIFAR-10, GTSRB, CelebA, and ImageNet12.The experiment results show that our attack approach can bypass most defense approaches, even for the inspections of humans. We only need poison 5%-10% training data, and the attack success rate(ASR) can reach over 99%. To test the robustness of the backdoor model against the corruption methods that in practice, we introduce 17 corruption methods and compute the accuracy, attack success rate(ASR) of the backdoor model. The facts show that the backdoor models generated by our approaches have strong robustness for the most corruption methods and can be applied in reality. Our code is available at https://github.com/928082786/pnoiseattack.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".