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Found 10 papers, 4 papers with code
Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
While efforts have been made to mitigate such problems, either by implementing a safety filter at the evaluation stage or by fine-tuning models to eliminate undesirable concepts or styles, the effectiveness of these safety measures in dealing with a wide range of prompts remains largely unexplored.
DPAF: Image Synthesis via Differentially Private Aggregation in Forward Phase
Differentially private synthetic data is a promising alternative for sensitive data release.
Catastrophic Data Leakage in Vertical Federated Learning
We name our proposed method as catastrophic data leakage in vertical federated learning (CAFE).
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations
Studying the sensitivity of weight perturbation in neural networks and its impacts on model performance, including generalization and robustness, is an active research topic due to its implications on a wide range of machine learning tasks such as model compression, generalization gap assessment, and adversarial attacks.
CAFE: Catastrophic Data Leakage in Vertical Federated Learning
We name our proposed method as catastrophic data leakage in vertical federated learning (CAFE).
Real-World Adversarial Examples involving Makeup Application
The Cycle-GAN is used to generate adversarial makeup, and the architecture of the victimized classifier is VGG 16.
Formalizing Generalization and Robustness of Neural Networks to Weight Perturbations
Studying the sensitivity of weight perturbation in neural networks and its impacts on model performance, including generalization and robustness, is an active research topic due to its implications on a wide range of machine learning tasks such as model compression, generalization gap assessment, and adversarial attacks.
Adversarial Examples can be Effective Data Augmentation for Unsupervised Machine Learning
In this paper, we propose a framework of generating adversarial examples for unsupervised models and demonstrate novel applications to data augmentation.
Non-Singular Adversarial Robustness of Neural Networks
In this paper, we formalize the notion of non-singular adversarial robustness for neural networks through the lens of joint perturbations to data inputs as well as model weights.
On The Utility of Conditional Generation Based Mutual Information for Characterizing Adversarial Subspaces
Recent studies have found that deep learning systems are vulnerable to adversarial examples; e. g., visually unrecognizable adversarial images can easily be crafted to result in misclassification.
