5 papers
SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning
Bac Nguyen, Stefan Uhlich, Fabien Cardinaux +3
Handling distribution shifts from training data, known as out-of-distribution (OOD) generalization, poses a significant challenge in the field of machine learning. While a pre-trai…
LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack
Ashkan Esmaeili, Marzieh Edraki, Nazanin Rahnavard +2
We propose LSDAT, an image-agnostic decision-based black-box attack that exploits low-rank and sparse decomposition (LSD) to dramatically reduce the number of queries and achieve s…
Odyssey: Creation, Analysis and Detection of Trojan Models
Marzieh Edraki, Nazmul Karim, Nazanin Rahnavard +2
Along with the success of deep neural network (DNN) models, rise the threats to the integrity of these models. A recent threat is the Trojan attack where an attacker interferes wit…
Subspace Capsule Network
Marzieh Edraki, Nazanin Rahnavard, Mubarak Shah
Convolutional neural networks (CNNs) have become a key asset to most of fields in AI. Despite their successful performance, CNNs suffer from a major drawback. They fail to capture…
CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces
Liheng Zhang, Marzieh Edraki, Guo-Jun Qi
In this paper, we formalize the idea behind capsule nets of using a capsule vector rather than a neuron activation to predict the label of samples. To this end, we propose to learn…