Defending Neural Backdoors via Generative Distribution Modeling

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Defending Neural Backdoors via Generative Distribution Modeling

10 October 2019

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Papers citing "Defending Neural Backdoors via Generative Distribution Modeling"

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Title
Blind Backdoors in Deep Learning Models Eugene Bagdasaryan Vitaly Shmatikov AAML FedML SILM 160 310 0 08 May 2020
Bias Busters: Robustifying DL-based Lithographic Hotspot Detectors Against Backdooring Attacks Kang Liu Benjamin Tan Gaurav Rajavendra Reddy S. Garg Yiorgos Makris Ramesh Karri AAML 49 9 0 26 Apr 2020
Rethinking the Trigger of Backdoor Attack Yiming Li Tongqing Zhai Baoyuan Wu Yong Jiang Zhifeng Li Shutao Xia LLMSV 104 152 0 09 Apr 2020
Towards Backdoor Attacks and Defense in Robust Machine Learning Models E. Soremekun Sakshi Udeshi Sudipta Chattopadhyay AAML 35 14 0 25 Feb 2020
NNoculation: Catching BadNets in the Wild A. Veldanda Kang Liu Benjamin Tan Prashanth Krishnamurthy Farshad Khorrami Ramesh Karri Brendan Dolan-Gavitt S. Garg AAML OnRL 82 20 0 19 Feb 2020
Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection Di Tang Xiaofeng Wang Haixu Tang Kehuan Zhang AAML 91 205 0 02 Aug 2019
Gotta Catch Ém All: Using Honeypots to Catch Adversarial Attacks on Neural Networks Shawn Shan Emily Wenger Bolun Wang Yangqiu Song Haitao Zheng Ben Y. Zhao 89 75 0 18 Apr 2019