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Found 5 papers, 0 papers with code
BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning
We hope that our efforts could build a solid foundation of backdoor learning to facilitate researchers to investigate existing algorithms, develop more innovative algorithms, and explore the intrinsic mechanism of backdoor learning.
Enhanced Few-Shot Class-Incremental Learning via Ensemble Models
To tackle overfitting challenge, we design a new ensemble model framework cooperated with data augmentation to boost generalization.
Defenses in Adversarial Machine Learning: A Survey
Adversarial phenomenon has been widely observed in machine learning (ML) systems, especially in those using deep neural networks, describing that ML systems may produce inconsistent and incomprehensible predictions with humans at some particular cases.
BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning
This paper reveals the threats in this practical scenario that backdoor attacks can remain effective even after defenses and introduces the \emph{\toolns} attack, which is resistant to backdoor detection and model fine-tuning defenses.
Enhancing Fine-Tuning Based Backdoor Defense with Sharpness-Aware Minimization
Fine-tuning based on benign data is a natural defense to erase the backdoor effect in a backdoored model.
