  Despite their outstanding performance in a broad spectrum of real-world
tasks, deep artificial neural networks are sensitive to input noises,
particularly adversarial perturbations. On the contrary, human and animal
brains are much less vulnerable. In contrast to the one-shot inference
performed by most deep neural networks, the brain often solves decision-making
with an evidence accumulation mechanism that may trade time for accuracy when
facing noisy inputs. The mechanism is well described by the Drift-Diffusion
Model (DDM). In the DDM, decision-making is modeled as a process in which noisy
evidence is accumulated toward a threshold. Drawing inspiration from the DDM,
we propose the Dropout-based Drift-Diffusion Model (DDDM) that combines
test-phase dropout and the DDM for improving the robustness for arbitrary
neural networks. The dropouts create temporally uncorrelated noises in the
network that counter perturbations, while the evidence accumulation mechanism
guarantees a reasonable decision accuracy. Neural networks enhanced with the
DDDM tested in image, speech, and text classification tasks all significantly
outperform their native counterparts, demonstrating the DDDM as a task-agnostic
defense against adversarial attacks.
