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08/22/2023
TOPIC: A Parallel Association Paradigm for Multi-Object Tracking under Complex Motions and Diverse Scenes
Video data and algorithms have been driving advances in multi-object tra...
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10/20/2022
FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information
Federated learning is vulnerable to poisoning attacks in which malicious...
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10/02/2022
FLCert: Provably Secure Federated Learning against Poisoning Attacks
Due to its distributed nature, federated learning is vulnerable to poiso...
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07/19/2022
FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients
Federated learning (FL) is vulnerable to model poisoning attacks, in whi...
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04/09/2022
A dataset of ant colonies motion trajectories in indoor and outdoor scenes for social cluster behavior study
Motion and interaction of social insects (such as ants) have been studie...
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03/16/2022
MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients
Existing model poisoning attacks to federated learning assume that an at...
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11/22/2021
Poisoning Attacks to Local Differential Privacy Protocols for Key-Value Data
Local Differential Privacy (LDP) protocols enable an untrusted server to...
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09/13/2021
FaceGuard: Proactive Deepfake Detection
Existing deepfake-detection methods focus on passive detection, i.e., th...
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07/06/2021
IGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control
Agriculture is the foundation of human civilization. However, the rapid ...
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07/05/2021
Understanding the Security of Deepfake Detection
Deepfakes pose growing challenges to the trust of information on the Int...
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02/03/2021
Provably Secure Federated Learning against Malicious Clients
Federated learning enables clients to collaboratively learn a shared glo...
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12/27/2020
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping
Byzantine-robust federated learning aims to enable a service provider to...
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12/07/2020
Certified Robustness of Nearest Neighbors against Data Poisoning Attacks
Data poisoning attacks aim to corrupt a machine learning model via modif...
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11/15/2020
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations
Top-k predictions are used in many real-world applications such as machi...
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08/24/2020
Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation
Graph neural networks (GNNs) have recently gained much attention for nod...
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08/11/2020
Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks
In a data poisoning attack, an attacker modifies, deletes, and/or insert...
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02/26/2020
On Certifying Robustness against Backdoor Attacks via Randomized Smoothing
Backdoor attack is a severe security threat to deep neural networks (DNN...
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02/09/2020
Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing
Community detection plays a key role in understanding graph structure. H...
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12/20/2019
Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing
It is well-known that classifiers are vulnerable to adversarial perturba...
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11/26/2019
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning
In federated learning, multiple client devices jointly learn a machine l...
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11/05/2019
Data Poisoning Attacks to Local Differential Privacy Protocols
Local Differential Privacy (LDP) protocols enable an untrusted data coll...
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10/28/2019
IPGuard: Protecting the Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundary
A deep neural network (DNN) classifier represents a model owner's intell...
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09/17/2017
