Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically\n Differentiable Renderer
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
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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