Search Results for author:
Found 9 papers, 3 papers with code
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive Models
However, recent research proposed poisoning attacks that cause a catastrophic loss in the accuracy of the global model when adversaries, posed as benign clients, are present in a group of clients.
DAFA: Distance-Aware Fair Adversarial Training
The disparity in accuracy between classes in standard training is amplified during adversarial training, a phenomenon termed the robust fairness problem.
New Insights for the Stability-Plasticity Dilemma in Online Continual Learning
The aim of continual learning is to learn new tasks continuously (i. e., plasticity) without forgetting previously learned knowledge from old tasks (i. e., stability).
PixelSteganalysis: Pixel-wise Hidden Information Removal with Low Visual Degradation
We propose a DL based steganalysis technique that effectively removes secret images by restoring the distribution of the original images.
AnomiGAN: Generative adversarial networks for anonymizing private medical data
We compared our method to state-of-the-art techniques and observed that our method preserves the same level of privacy as differential privacy (DP), but had better prediction results.
PixelSteganalysis: Destroying Hidden Information with a Low Degree of Visual Degradation
The cover image with the secret message is called a stego image.
Security and Privacy Issues in Deep Learning
Furthermore, the privacy of the data involved in model training is also threatened by attacks such as the model-inversion attack, or by dishonest service providers of AI applications.
Quantized Memory-Augmented Neural Networks
In this paper, we identify memory addressing (specifically, content-based addressing) as the main reason for the performance degradation and propose a robust quantization method for MANNs to address the challenge.
DNA Steganalysis Using Deep Recurrent Neural Networks
We compare our proposed method to various existing methods and biological sequence analysis methods implemented on top of our framework.
