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Found 5 papers, 0 papers with code
MUTE-SLAM: Real-Time Neural SLAM with Multiple Tri-Plane Hash Representations
MUTE-SLAM effectively tracks camera positions and incrementally builds a scalable multi-map representation for both small and large indoor environments.
Rethinking White-Box Watermarks on Deep Learning Models under Neural Structural Obfuscation
Copyright protection for deep neural networks (DNNs) is an urgent need for AI corporations.
Matryoshka: Stealing Functionality of Private ML Data by Hiding Models in Model
In this paper, we present a novel insider attack called Matryoshka, which employs an irrelevant scheduled-to-publish DNN model as a carrier model for covert transmission of multiple secret models which memorize the functionality of private ML data stored in local data centers.
Cracking White-box DNN Watermarks via Invariant Neuron Transforms
On $9$ state-of-the-art white-box watermarking schemes and a broad set of industry-level DNN architectures, our attack for the first time reduces the embedded identity message in the protected models to be almost random.
Exploring the Security Boundary of Data Reconstruction via Neuron Exclusivity Analysis
Among existing privacy attacks on the gradient of neural networks, \emph{data reconstruction attack}, which reverse engineers the training batch from the gradient, poses a severe threat on the private training data.
