Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer
Paper summary Liu et al. propose adversarial attacks on physical parameters of images, which can be manipulated efficiently through differentiable renderer. In particular, they propose adversarial lighting and adversarial geometry; in both cases, an image is assumed to be a function of lighting and geometry, generated by a differentiable renderer. By directly manipulating these latent variables, more realistic looking adversarial examples can be generated for synthetic images as shown in Figure 1. https://i.imgur.com/uh2pj9w.png Figure 1: Comparison of the proposed attack with known attacks applied to large perturbations, $L_\infty \approx 0.82$. Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).
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Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer
Hsueh-Ti Derek Liu and Michael Tao and Chun-Liang Li and Derek Nowrouzezahrai and Alec Jacobson
arXiv e-Print archive - 2018 via Local arXiv
Keywords: cs.LG, cs.CV, cs.GR, stat.ML

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Summary by David Stutz 2 months ago
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