  Targeted adversarial attack, which aims to mislead a model to recognize any
image as a target object by imperceptible perturbations, has become a
mainstream tool for vulnerability assessment of deep neural networks (DNNs).
Since existing targeted attackers only learn to attack known target classes,
they cannot generalize well to unknown classes. To tackle this issue, we
propose $\bf{G}$eneralized $\bf{A}$dversarial attac$\bf{KER}$ ($\bf{GAKer}$),
which is able to construct adversarial examples to any target class. The core
idea behind GAKer is to craft a latently infected representation during
adversarial example generation. To this end, the extracted latent
representations of the target object are first injected into intermediate
features of an input image in an adversarial generator. Then, the generator is
optimized to ensure visual consistency with the input image while being close
to the target object in the feature space. Since the GAKer is class-agnostic
yet model-agnostic, it can be regarded as a general tool that not only reveals
the vulnerability of more DNNs but also identifies deficiencies of DNNs in a
wider range of classes. Extensive experiments have demonstrated the
effectiveness of our proposed method in generating adversarial examples for
both known and unknown classes. Notably, compared with other generative
methods, our method achieves an approximately $14.13\%$ higher attack success
rate for unknown classes and an approximately $4.23\%$ higher success rate for
known classes. Our code is available in https://github.com/VL-Group/GAKer.
