A Feature-Based On-Line Detector to Remove Adversarial-Backdoors by Iterative Demarcation
Bibliographic record
Abstract
This paper proposes a novel feature-based on-line detection strategy, Removing Adversarial-Backdoors by Iterative Demarcation (RAID), for backdoor attacks. The proposed method is comprised of two parts: off-line training and on-line retraining. In the off-line training, a novelty detector and a shallow neural network are trained with clean validation data. During the on-line implementation, both models attempt to detect samples from the streaming data that differ from the validation data (i.e., flag likely-poisoned samples and possibly a few clean samples as false positives). An anomaly detector is used to purify the anomalous data by removing the clean samples. A binary support vector machine (SVM) is trained with the purified anomalous data and the clean validation data. RAID uses the SVM to detect poisoned inputs. To increase the accuracy as new anomalous data is being detected, the SVM is updated as well in real-time. It is shown that with updating, RAID can efficiently reduce the attack success rate while maintaining the classification accuracy against various types of backdoor attacks. The efficacy of RAID is compared against several state-of-the-art techniques. Additionally, it is shown that RAID only requires a small clean validation dataset to achieve such performance, and therefore provides a practical and efficient approach.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".