-
Proof-of-Learning: Definitions and Practice
Training machine learning (ML) models typically involves expensive itera...
03/09/2021 ∙ by Hengrui Jia, et al. ∙
11
∙
share
read it
-
Detecting Anomalous Inputs to DNN Classifiers By Joint Statistical Testing at the Layers
Detecting anomalous inputs, such as adversarial and out-of-distribution ...
07/29/2020 ∙ by Jayaram Raghuram, et al. ∙
16
∙
share
read it
-
Face-Off: Adversarial Face Obfuscation
Advances in deep learning have made face recognition increasingly feasib...
03/19/2020 ∙ by Chuhan Gao, et al. ∙
0
∙
share
read it
-
On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping
Machine learning algorithms are vulnerable to data poisoning attacks. Pr...
02/26/2020 ∙ by Sanghyun Hong, et al. ∙
0
∙
share
read it
-
Machine Unlearning
Once users have shared their data online, it is generally difficult for ...
12/09/2019 ∙ by Lucas Bourtoule, et al. ∙
53
∙
share
read it
-
Generating Semantic Adversarial Examples with Differentiable Rendering
Machine learning (ML) algorithms, especially deep neural networks, have ...
10/02/2019 ∙ by Lakshya Jain, et al. ∙
23
∙
share
read it
-
Enhancing ML Robustness Using Physical-World Constraints
Recent advances in Machine Learning (ML) have demonstrated that neural n...
05/26/2019 ∙ by Varun Chandrasekaran, et al. ∙
0
∙
share
read it
-
Characterizing Privacy Perceptions of Voice Assistants: A Technology Probe Study
The increasing pervasiveness of voice assistants in the home poses sever...
12/01/2018 ∙ by Varun Chandrasekaran, et al. ∙
0
∙
share
read it
-
Model Extraction and Active Learning
Machine learning is being increasingly used by individuals, research ins...
11/05/2018 ∙ by Varun Chandrasekaran, et al. ∙
14
∙
share
read it
