IAM

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.

What is your opinion on the summarized work? Or do you know related work that is of interest? Let me know your thoughts in the comments below: