  We propose CLEANN, the first end-to-end framework that enables online
mitigation of Trojans for embedded Deep Neural Network (DNN) applications. A
Trojan attack works by injecting a backdoor in the DNN while training; during
inference, the Trojan can be activated by the specific backdoor trigger. What
differentiates CLEANN from the prior work is its lightweight methodology which
recovers the ground-truth class of Trojan samples without the need for labeled
data, model retraining, or prior assumptions on the trigger or the attack. We
leverage dictionary learning and sparse approximation to characterize the
statistical behavior of benign data and identify Trojan triggers. CLEANN is
devised based on algorithm/hardware co-design and is equipped with specialized
hardware to enable efficient real-time execution on resource-constrained
embedded platforms. Proof of concept evaluations on CLEANN for the
state-of-the-art Neural Trojan attacks on visual benchmarks demonstrate its
competitive advantage in terms of attack resiliency and execution overhead.
