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Found 9 papers, 2 papers with code
Universal Adversarial Perturbations for Vision-Language Pre-trained Models
To this end, we propose a novel black-box method to generate Universal Adversarial Perturbations (UAPs), which is so called the Effective and T ransferable Universal Adversarial Attack (ETU), aiming to mislead a variety of existing VLP models in a range of downstream tasks.
Effective and Robust Adversarial Training against Data and Label Corruptions
We propose a hybrid adversarial training surrounding multiple potential adversarial perturbations, alongside a semi-supervised learning based on class- rebalancing sample selection to enhance the resilience of the model for dual corruption.
PAODING: A High-fidelity Data-free Pruning Toolkit for Debloating Pre-trained Neural Networks
We present PAODING, a toolkit to debloat pretrained neural network models through the lens of data-free pruning.
Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors
Vulnerability detectors based on deep learning (DL) models have proven their effectiveness in recent years.
AGRAMPLIFIER: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification
By equipping AGRAMPLIFIER with the existing Byzantine-robust mechanisms, we successfully enhance the model's robustness, maintaining its fidelity and improving overall efficiency.
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective
For those reasons, there is a high demand for trustworthy and rigorous methods to verify the robustness of neural network models.
Supervised Robustness-preserving Data-free Neural Network Pruning
This may be unrealistic in practice, as the data controllers are often reluctant to provide their model consumers with the original data.
Confined Gradient Descent: Privacy-preserving Optimization for Federated Learning
We prove that less information is exposed in CGD compared to that of traditional FL.
TwiInsight: Discovering Topics and Sentiments from Social Media Datasets
Social media platforms contain a great wealth of information which provides opportunities for us to explore hidden patterns or unknown correlations, and understand people's satisfaction with what they are discussing.
