  Recent works have demonstrated the existence of {\it adversarial examples}
targeting a single machine learning system. In this paper we ask a simple but
fundamental question of "selective fooling": given {\it multiple} machine
learning systems assigned to solve the same classification problem and taking
the same input signal, is it possible to construct a perturbation to the input
signal that manipulates the outputs of these {\it multiple} machine learning
systems {\it simultaneously} in arbitrary pre-defined ways? For example, is it
possible to selectively fool a set of "enemy" machine learning systems but does
not fool the other "friend" machine learning systems? The answer to this
question depends on the extent to which these different machine learning
systems "think alike". We formulate the problem of "selective fooling" as a
novel optimization problem, and report on a series of experiments on the MNIST
dataset. Our preliminary findings from these experiments show that it is in
fact very easy to selectively manipulate multiple MNIST classifiers
simultaneously, even when the classifiers are identical in their architectures,
training algorithms and training datasets except for random initialization
during training. This suggests that two nominally equivalent machine learning
systems do not in fact "think alike" at all, and opens the possibility for many
novel applications and deeper understandings of the working principles of deep
neural networks.
