no code implementations • 17 Sep 2020 • Sumit Kumar Jha, Susmit Jha, Rickard Ewetz, Sunny Raj, Alvaro Velasquez, Laura L. Pullum, Ananthram Swami
We present a new extension of Fano's inequality and employ it to theoretically establish that the probability of success for a membership inference attack on a deep neural network can be bounded using the mutual information between its inputs and its activations.
1 code implementation • NeurIPS 2019 • Susmit Jha, Sunny Raj, Steven Fernandes, Sumit K. Jha, Somesh Jha, Brian Jalaian, Gunjan Verma, Ananthram Swami
These experiments demonstrate the effectiveness of the ABC metric to make DNNs more trustworthy and resilient.
no code implementations • 14 Mar 2019 • Susmit Jha, Sunny Raj, Steven Lawrence Fernandes, Sumit Kumar Jha, Somesh Jha, Gunjan Verma, Brian Jalaian, Ananthram Swami
We study the robustness of machine learning models on benign and adversarial inputs in this neighborhood.