Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically\n Differentiable Renderer
Bibliographic record
Abstract
Many machine learning image classifiers are vulnerable to adversarial\nattacks, inputs with perturbations designed to intentionally trigger\nmisclassification. Current adversarial methods directly alter pixel colors and\nevaluate against pixel norm-balls: pixel perturbations smaller than a specified\nmagnitude, according to a measurement norm. This evaluation, however, has\nlimited practical utility since perturbations in the pixel space do not\ncorrespond to underlying real-world phenomena of image formation that lead to\nthem and has no security motivation attached. Pixels in natural images are\nmeasurements of light that has interacted with the geometry of a physical\nscene. As such, we propose the direct perturbation of physical parameters that\nunderly image formation: lighting and geometry. As such, we propose a novel\nevaluation measure, parametric norm-balls, by directly perturbing physical\nparameters that underly image formation. One enabling contribution we present\nis a physically-based differentiable renderer that allows us to propagate pixel\ngradients to the parametric space of lighting and geometry. Our approach\nenables physically-based adversarial attacks, and our differentiable renderer\nleverages models from the interactive rendering literature to balance the\nperformance and accuracy trade-offs necessary for a memory-efficient and\nscalable adversarial data augmentation workflow.\n
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".