Tag: adversarial robustness

Researchers at Donghua University have developed RFLDefense, a layer-wise defense framework that suppresses backdoor attacks in federated learning even when ...

A new framework called VR-FraudNet forces large language models to propose structured fraud explanations that a fixed deterministic verifier must ...

Researchers have built a hybrid malware detection framework combining abstract syntax trees, L-moments, graph analysis, and quantum-inspired neural networks that ...

A new blockchain-enabled federated learning framework called IoV BCFL+ detects intrusions across connected vehicle networks with over 96 percent accuracy ...

A new causal graph neural network filters out camouflaged neighbour signals in transaction graphs, outperforming seven baselines on Bitcoin, YelpChi, ...

A controlled comparison shows attention-enhanced DenseNet121 reaching 97.4 percent accuracy for deepfake detection while a lighter MobileNetV2 variant offers a ...

Researchers in India have built a multimodal deep learning framework that fuses text, images and browsing behavior to classify dark ...

Researchers at Guizhou Power Grid show that a compact large language model grounded in a digital power grid knowledge base ...

A systematic review of 93 studies reveals eight attack types and nine defence types across six categories of unsupervised machine ...

Researchers have developed a federated, lightweight deep learning system that detects attacks on car CAN bus networks with high accuracy ...