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Found 4 papers, 2 papers with code
Blackbox Attacks via Surrogate Ensemble Search
We also show that the perturbations generated by our proposed method are highly transferable and can be adopted for hard-label blackbox attacks.
Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency
There has been a recent surge in research on adversarial perturbations that defeat Deep Neural Networks (DNNs) in machine vision; most of these perturbation-based attacks target object classifiers.
Non-Uniform Subset Selection for Active Learning in Structured Data
We construct a graph from the unlabeled data to represent the underlying structure, such that each node represents a data point, and edges represent the inter-relationships between them.
Adaptive Algorithm and Platform Selection for Visual Detection and Tracking
In this paper, we focus on this problem and propose a framework to adaptively select the "best" algorithm-parameter combination and the computation platform under performance and cost constraints at design time, and adapt the algorithms at runtime based on real-time inputs.
