Moosavi-Dezfooli et al. propose universal adversarial perturbations – perturbations that are image-agnostic. Specifically, they extend the framework for crafting adversarial examples, i.e. by iteratively solving
$\arg\min_r \|r \|_2$ s.t. $f(x + r) \neq f(x)$.
Here, $r$ denotes the adversarial perturbation, $x$ a training sample and $f$ the neural network. Instead of solving this problem for a specific $x$, the authors propose to solve the problem over the full training set, i.e. in each iteration, a different sample $x$ is chosen, one step in the direction of the gradient is taken and the perturbation is updated accordingly. In experiments, they show that these universal perturbations are indeed able to fool networks an several images; in addition, these perturbations are – sometimes – transferable to other networks.
Moosavi-Dezfooli et al. propose universal adversarial perturbations – perturbations that are image-agnostic. Specifically, they extend the framework for crafting adversarial examples, i.e. by iteratively solving
$\arg\min_r \|r \|_2$ s.t. $f(x + r) \neq f(x)$.
Here, $r$ denotes the adversarial perturbation, $x$ a training sample and $f$ the neural network. Instead of solving this problem for a specific $x$, the authors propose to solve the problem over the full training set, i.e. in each iteration, a different sample $x$ is chosen, one step in the direction of the gradient is taken and the perturbation is updated accordingly. In experiments, they show that these universal perturbations are indeed able to fool networks an several images; in addition, these perturbations are – sometimes – transferable to other networks.