  The generation of adversarial inputs has become a crucial issue in
establishing the robustness and trustworthiness of deep neural nets, especially
when they are used in safety-critical application domains such as autonomous
vehicles and precision medicine. However, the problem poses multiple practical
challenges, including scalability issues owing to large-sized networks, and the
generation of adversarial inputs that lack important qualities such as
naturalness and output-impartiality. This problem shares its end goal with the
task of patching neural nets where small changes in some of the network's
weights need to be discovered so that upon applying these changes, the modified
net produces the desirable output for a given set of inputs. We exploit this
connection by proposing to obtain an adversarial input from a patch, with the
underlying observation that the effect of changing the weights can also be
brought about by changing the inputs instead. Thus, this paper presents a novel
way to generate input perturbations that are adversarial for a given network by
using an efficient network patching technique. We note that the proposed method
is significantly more effective than the prior state-of-the-art techniques.
