Deep neural networks (DNNs) have progressed rapidly during the past decade
and have been deployed in various real-world applications. Meanwhile, DNN
models have been shown to be vulnerable to security and privacy attacks. One
such attack that has attracted a great deal of attention recently is the
backdoor attack. Specifically, the adversary poisons the target model’s
training set to mislead any input with an added secret trigger to a target

360 Mobile Vision - North & South Carolina Security products and Systems Installations for Commercial and Residential - $55 Hourly Rate. ACCESS CONTROL, INTRUSION ALARM, ACCESS CONTROLLED GATES, INTERCOMS AND CCTV INSTALL OR REPAIR 360 Mobile Vision - is committed to excellence in every aspect of our business. We uphold a standard of integrity bound by fairness, honesty and personal responsibility. Our distinction is the quality of service we bring to our customers. Accurate knowledge of our trade combined with ability is what makes us true professionals. Above all, we are watchful of our customers interests, and make their concerns the basis of our business.

Previous backdoor attacks predominantly focus on computer vision (CV)
applications, such as image classification. In this paper, we perform a
systematic investigation of backdoor attack on NLP models, and propose BadNL, a
general NLP backdoor attack framework including novel attack methods.
Specifically, we propose three methods to construct triggers, namely BadChar,
BadWord, and BadSentence, including basic and semantic-preserving variants. Our
attacks achieve an almost perfect attack success rate with a negligible effect
on the original model’s utility. For instance, using the BadChar, our backdoor
attack achieves a 98.9% attack success rate with yielding a utility improvement
of 1.5% on the SST-5 dataset when only poisoning 3% of the original set.
Moreover, we conduct a user study to prove that our triggers can well preserve
the semantics from humans perspective.

By admin