Backdoor attacks aim to mislead machine-learning models to output an
attacker-specified class when presented a specific trigger at test time. These
attacks require poisoning the training data or compromising the learning
algorithm, e.g., by injecting poisoning samples containing the trigger into the
training set, along with the desired class label. Despite the increasing number
of studies on backdoor attacks and defenses, the underlying factors affecting
the success of backdoor attacks, along with their impact on the learning
algorithm, are not yet well understood. In this work, we aim to shed light on
this issue. In particular, we unveil that backdoor attacks work by inducing a
smoother decision function around the triggered samples — a phenomenon which
we refer to as textit{backdoor smoothing}. We quantify backdoor smoothing by
defining a measure that evaluates the uncertainty associated to the predictions
of a classifier around the input samples.

Our experiments show that smoothness increases when the trigger is added to
the input samples, and that the phenomenon is more pronounced for more
successful attacks.

However, our experiments also show that patterns fulfilling backdoor
smoothing can be crafted

even without poisoning the training data.

Although our measure may not be directly exploited as a defense mechanism, it
unveils an important phenomenon which may pave the way towards understanding
the limitations of current defenses that rely on a smooth decision output for

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