IAM

DAVIDSTUTZ

19thSEPTEMBER2019

READING

Hsueh-Ti Derek Liu, Michael Tao, Chun-Liang Li, Derek Nowrouzezahrai, Alec Jacobson. Adversarial Geometry and Lighting using a Differentiable Renderer. CoRR abs/1808.02651 (2018).

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.

Figure 1: Comparison of the proposed attack with known attacks applied to large perturbations, $L_\infty \approx 0.82$.

Also find this summary on ShortScience.org.
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