Search Results for author:
Found 8 papers, 2 papers with code
Removing Undesirable Concepts in Text-to-Image Generative Models with Learnable Prompts
Generative models have demonstrated remarkable potential in generating visually impressive content from textual descriptions.
Transferable Graph Backdoor Attack
In this paper, we disclose the TRAP attack, a Transferable GRAPh backdoor attack.
Learning to Attack with Fewer Pixels: A Probabilistic Post-hoc Framework for Refining Arbitrary Dense Adversarial Attacks
Deep neural network image classifiers are reported to be susceptible to adversarial evasion attacks, which use carefully crafted images created to mislead a classifier.
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness
An important technique of this approach is to control the transferability of adversarial examples among ensemble members.
Improving Adversarial Robustness by Enforcing Local and Global Compactness
The fact that deep neural networks are susceptible to crafted perturbations severely impacts the use of deep learning in certain domains of application.
Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions
Deep neural network image classifiers are reported to be susceptible to adversarial evasion attacks, which use carefully crafted images created to mislead a classifier.
Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence
Recent studies have demonstrated that reinforcement learning (RL) agents are susceptible to adversarial manipulation, similar to vulnerabilities previously demonstrated in the supervised learning setting.
Reinforcement Learning for Autonomous Defence in Software-Defined Networking
Despite the successful application of machine learning (ML) in a wide range of domains, adaptability---the very property that makes machine learning desirable---can be exploited by adversaries to contaminate training and evade classification.
