SparseFool
Geometry-inspired sparse attack on deep networks
Deep Neural Networks have achieved extraordinary results on image classification tasks, but have been shown to be vulnerable to attacks with carefully crafted perturbations of the input data. Although most attacks usually change values of many image’s pixels, it has been shown that deep networks are also vulnerable to sparse alterations of the input. SparseFool implements an efficient algorithm to compute and control sparse alterations.
Deep Neural Networks
- Technical
- Research papers
- Source code: Lab GitHub
- Last commit: 2020-09-27
The Signal Processing Laboratory (LTS4) is a team of researchers led by Prof. Pascal Frossard, working in the Electrical Engineering Institute of the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
The group research focuses on image processing, graph signal processing and machine learning, as well as closely related fields such as network data analysis, distributed signal processing, image and video coding and immersive communications. We work at the frontier between signal processing, machine learning and applied mathematics.
The group research focuses on image processing, graph signal processing and machine learning, as well as closely related fields such as network data analysis, distributed signal processing, image and video coding and immersive communications. We work at the frontier between signal processing, machine learning and applied mathematics.
This page was last edited on 2024-03-21.
This page was last edited on 2024-03-21.