First-Order Efficient General-Purpose Clean-Label Data Poisoning
Why this work is in the frame
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Bibliographic record
Abstract
As one of the recently emerged threats to Deep Learning (DL) models, clean-label data poisoning can teach DL models to make wrong predictions on specific target data, such as images or network traffic packets, by injecting a small set of poisoning data with clean labels into the training datasets. Although several clean-label poisoning methods have been developed before, they have two main limitations. First, the methods developed with bi-level optimization or influence functions usually require second-order information, leading to substantial computational overhead. Second, the methods based on feature collision are not very transferable to unseen feature spaces or generalizable to various scenarios. To address these limitations, we propose a first-order efficient general-purpose clean-label poisoning attack in this paper. In our attack, we first identify the first-order model update that can push the model towards predicting the target data as the attack targeted label. We then formulate a necessary condition based on the model update and other first-order information to optimize the poisoning data. Theoretically, we prove that our first-order poisoning method is an approximation of a second-order approach with theoretically-guaranteed performance. Empirically, extensive evaluations on image classification and network traffic classification demonstrate the outstanding efficiency, transferability, and generalizability of our poisoning method.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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