Designing Perceptual Puzzles by Differentiating Probabilistic Programs

Kartik Chandra, Tzu-Mao Li, Joshua B. Tenenbaum, Jonathan Ragan-Kelley

Abstract: We design new visual illusions by finding "adversarial examples" for principled models of human perception -- specifically, for probabilistic models, which treat vision as Bayesian inference. To perform this search efficiently, we design a differentiable probabilistic programming language, whose API exposes MCMC inference as a first-class differentiable function. We demonstrate our method by automatically creating illusions for three features of human vision: color constancy, size constancy, and face perception.