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"Towards Effective and Robust Neural Trojan Defenses via Input Filterin" by Kien Do, Haripriya Harikumar et al.

Trojan attacks on deep neural networks are both dangerous and surreptitious. Over the past few years, Trojan attacks have advanced from using only a single input-agnostic trigger and targeting only one class to using multiple, input-specific triggers and targeting multiple classes. However, Trojan defenses have not caught up with this development. Most defense methods still make inadequate assumptions about Trojan triggers and target classes, thus, can be easily circumvented by modern Trojan attacks. To deal with this problem, we propose two novel “filtering” defenses called Variational Input Filtering (VIF) and Adversarial Input Filtering (AIF) which leverage lossy data compression and adversarial learning respectively to effectively purify potential Trojan triggers in the input at run time without making assumptions about the number of triggers/target classes or the input dependence property of triggers. In addition, we introduce a new defense mechanism called “Filtering-then-C ....

Variational Input Filtering , Adversarial Input Filtering ,

AI trojan horse techniques outpace defenses, study finds

AI trojan horse techniques outpace defenses, study finds
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United States , Artificial Intelligence Institute At Deakin University , Texasam University , University Of Wollongong , Emergen Research , Applied Artificial Intelligence Institute , Deakin University , Robust Neural Trojan Defenses , Input Filtering , Variational Input Filtering , Adversarial Input Filtering ,