On The Generation of Unrestricted Adversarial Examples
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
Adversarial examples are inputs designed by an adversary with the goal of fooling the machine learning models. Most of the research about adversarial examples have focused on perturbing the natural inputs with the assumption that the true label remains unchanged. Even in this limited setting and despite extensive studies in recent years, there is no defence against adversarial examples for complex tasks (e.g., ImageNet). However, for simpler tasks like handwritten digit classification, a robust model seems to be within reach. Unlike perturbation-based adversarial examples, the adversary is not limited to small norm-based perturbations in unrestricted adversarial examples. Hence, defending against unrestricted adversarial examples is a more challenging task. In this paper, we show that previous methods for generating unrestricted adversarial examples ignored a large part of the adversarial subspace. In particular, we demonstrate the bias of previous methods towards generating samples that are far inside the decision boundaries of an auxiliary classifier. We also show the similarity of the decision boundaries of an auxiliary classifier and baseline CNNs. By putting these two evidence together, we explain why adversarial examples generated by the previous approaches lack the desired transferability. Additionally, we present an efficient technique to create adversarial examples using generative adversarial networks to address this issue. We demonstrate that even the state-of-the-art MNIST classifiers are vulnerable to the adversarial examples generated with this technique. Additionally, we show that examples generated with our method are transferable. Accordingly, we hope that new proposed defences use this attack to evaluate the robustness of their models against unrestricted attacks.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it