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Found 7 papers, 3 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.
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.
VDC: Versatile Data Cleanser based on Visual-Linguistic Inconsistency by Multimodal Large Language Models
The role of data in building AI systems has recently been emphasized by the emerging concept of data-centric AI.
Boosting Backdoor Attack with A Learnable Poisoning Sample Selection Strategy
To further integrate it with normal training process, we then propose a learnable poisoning sample selection strategy to learn the mask together with the model parameters through a min-max optimization. Specifically, the outer loop aims to achieve the backdoor attack goal by minimizing the loss based on the selected samples, while the inner loop selects hard poisoning samples that impede this goal by maximizing the loss.
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.
Mean Parity Fair Regression in RKHS
We study the fair regression problem under the notion of Mean Parity (MP) fairness, which requires the conditional mean of the learned function output to be constant with respect to the sensitive attributes.
BackdoorBench: A Comprehensive Benchmark of Backdoor Learning
However, we find that the evaluations of new methods are often unthorough to verify their claims and accurate performance, mainly due to the rapid development, diverse settings, and the difficulties of implementation and reproducibility.
