Search Results for author:
Found 7 papers, 5 papers with code
Towards Generation and Evaluation of Comprehensive Mapping Robot Datasets
This paper presents a fully hardware synchronized mapping robot with support for a hardware synchronized external tracking system, for super-precise timing and localization.
Robotics
Hidden Backdoors in Human-Centric Language Models
We are able to demonstrate the adversary's high success rate of attacks, while maintaining functionality for regular users, with triggers inconspicuous by the human administrators.
Fingerprinting Multi-exit Deep Neural Network Models via Inference Time
Specifically, we design an effective method to generate a set of fingerprint samples to craft the inference process with a unique and robust inference time cost as the evidence for model ownership.
Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors
We find that (i) commercial antivirus engines are vulnerable to AMM-guided test cases; (ii) the ability of a manipulated malware generated using one detector to evade detection by another detector (i. e., transferability) depends on the overlap of features with large AMM values between the different detectors; and (iii) AMM values effectively measure the fragility of features (i. e., capability of feature-space manipulation to flip the prediction results) and explain the robustness of malware detectors facing evasion attacks.
An Interpretable Federated Learning-based Network Intrusion Detection Framework
Learning-based Network Intrusion Detection Systems (NIDSs) are widely deployed for defending various cyberattacks.
Privacy for Free: How does Dataset Condensation Help Privacy?
In this work, we for the first time identify that dataset condensation (DC) which is originally designed for improving training efficiency is also a better solution to replace the traditional data generators for private data generation, thus providing privacy for free.
Mind Your Heart: Stealthy Backdoor Attack on Dynamic Deep Neural Network in Edge Computing
Transforming off-the-shelf deep neural network (DNN) models into dynamic multi-exit architectures can achieve inference and transmission efficiency by fragmenting and distributing a large DNN model in edge computing scenarios (e. g., edge devices and cloud servers).
