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Found 7 papers, 5 papers with code
Towards Fairness-Aware Adversarial Learning
As a generalization of conventional AT, we re-define the problem of adversarial training as a min-max-max framework, to ensure both robustness and fairness of the trained model.
Reward Certification for Policy Smoothed Reinforcement Learning
Reinforcement Learning (RL) has achieved remarkable success in safety-critical areas, but it can be weakened by adversarial attacks.
A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation
Large Language Models (LLMs) have exploded a new heatwave of AI for their ability to engage end-users in human-level conversations with detailed and articulate answers across many knowledge domains.
Dynamic Efficient Adversarial Training Guided by Gradient Magnitude
Therefore, based on the magnitude of the gradient, we propose a general acceleration strategy, M+ acceleration, which enables an automatic and highly effective method of adjusting the training procedure.
Fooling Object Detectors: Adversarial Attacks by Half-Neighbor Masks
Although there are a great number of adversarial attacks on deep learning based classifiers, how to attack object detection systems has been rarely studied.
Generalizing Universal Adversarial Attacks Beyond Additive Perturbations
Extensive experiments are conducted on CIFAR-10 and ImageNet datasets with six deep neural network models including GoogleLeNet, VGG16/19, ResNet101/152, and DenseNet121.
Collaboratively Weighting Deep and Classic Representation via L2 Regularization for Image Classification
We propose a deep collaborative weight-based classification (DeepCWC) method to resolve this problem, by providing a novel option to fully take advantage of deep features in classic machine learning.
