Use Procedural Noise to Achieve Backdoor Attack
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
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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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 | 0.001 |
| 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 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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