Search Results for author:
Found 6 papers, 3 papers with code
LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack
It is set forth that the proposed sparse perturbation is the most aligned sparse perturbation with the shortest path from the input sample to the decision boundary for some initial adversarial sample (the best sparse approximation of shortest path, likely to fool the model).
Odyssey: Creation, Analysis and Detection of Trojan Models
We propose a detector that is based on the analysis of the intrinsic DNN properties; that are affected due to the Trojaning process.
Subspace Capsule Network
In this paper, we propose the SubSpace Capsule Network (SCN) that exploits the idea of capsule networks to model possible variations in the appearance or implicitly defined properties of an entity through a group of capsule subspaces instead of simply grouping neurons to create capsules.
Generalized Loss-Sensitive Adversarial Learning with Manifold Margins
Such a manifold assumption suggests the distance over the manifold should be a better measure to characterize the distinct between real and fake sam- ples.
CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces
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.
Global versus Localized Generative Adversarial Nets
In this paper, we present a novel localized Generative Adversarial Net (GAN) to learn on the manifold of real data.
