Towards robust adversarial examples for deep neural networks
Bibliographic record
Abstract
In this paper, we show two methods to compute sampling-robust adversarial examples (AEs) for deep neural networks with rectilinear units (DNNs).Both methods use an adjustable robust counterpart of a MILP model by Fischetti an Jo.They rely on new uncertainty sets in (pseudo-)metric spaces of DNNs with identical structure and compact inputs.One method (the inner method) needs full information on weights and biases of a nominal DNN after training.The other one (the outer method) only needs full information on the training data and the training method used.We compare the two methods in experiments on DNNs classifying small fashion images according to the type of apparel shown.While the inner method generates AEs that are only robust w.r.t.very mild retraining of a DNN, the outer method leads to AEs that are robust w.r.t.retraining from scratch on the same training data.The outer approach can therefore in principle be used for grey-box attacks of DNNs with no knowledge on internal parameters after training.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".