Search Results for author:
Found 23 papers, 13 papers with code
Personalized Privacy Protection Mask Against Unauthorized Facial Recognition
Second, we incorporate a perceptibility optimization to preserve the visual quality of the protected facial images.
On the Robustness of Graph Reduction Against GNN Backdoor
Meanwhile, graph reduction techniques, including coarsening and sparsification, which have long been employed to improve the scalability of large graph computational tasks, have recently emerged as effective methods for accelerating GNN training on large-scale graphs.
On the Efficiency of Privacy Attacks in Federated Learning
Second, we propose three early-stopping techniques to effectively reduce the computational costs of these privacy attacks.
Robust Few-Shot Ensemble Learning with Focal Diversity-Based Pruning
This paper presents FusionShot, a focal diversity optimized few-shot ensemble learning approach for boosting the robustness and generalization performance of pre-trained few-shot models.
A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective
Vertical Federated Learning (VFL) is a federated learning paradigm where multiple participants, who share the same set of samples but hold different features, jointly train machine learning models.
Imperio: Language-Guided Backdoor Attacks for Arbitrary Model Control
This paper proposes Imperio, which harnesses the language understanding capabilities of NLP models to enrich backdoor attacks.
Hierarchical Pruning of Deep Ensembles with Focal Diversity
This paper presents a novel deep ensemble pruning approach, which can efficiently identify smaller deep ensembles and provide higher ensemble accuracy than the entire deep ensemble of a large number of member networks.
Exploring Model Learning Heterogeneity for Boosting Ensemble Robustness
We show that this two-tier heterogeneity driven ensemble construction method can compose an ensemble team that promotes high ensemble diversity and low negative correlation among member models of the ensemble, strengthening ensemble robustness against both negative examples and adversarial attacks.
Securing Distributed SGD against Gradient Leakage Threats
Next, we present a gradient leakage resilient approach to securing distributed SGD in federated learning, with differential privacy controlled noise as the tool.
STDLens: Model Hijacking-Resilient Federated Learning for Object Detection
Based on the insights, we introduce a three-tier forensic framework to identify and expel Trojaned gradients and reclaim the performance over the course of FL.
Adaptive Deep Neural Network Inference Optimization with EENet
Instead of having every sample go through all DNN layers during prediction, EENet learns an early exit scheduler, which can intelligently terminate the inference earlier for certain predictions, which the model has high confidence of early exit.
Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics
Our new metrics significantly improve the intrinsic correlation between high ensemble diversity and high ensemble accuracy.
Promoting High Diversity Ensemble Learning with EnsembleBench
Ensemble learning is gaining renewed interests in recent years.
Understanding Object Detection Through An Adversarial Lens
We demonstrate that the proposed framework can serve as a methodical benchmark for analyzing adversarial behaviors and risks in real-time object detection systems.
LDP-Fed: Federated Learning with Local Differential Privacy
However, in federated learning model parameter updates are collected iteratively from each participant and consist of high dimensional, continuous values with high precision (10s of digits after the decimal point), making existing LDP protocols inapplicable.
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning
FL offers default client privacy by allowing clients to keep their sensitive data on local devices and to only share local training parameter updates with the federated server.
TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems
The rapid growth of real-time huge data capturing has pushed the deep learning and data analytic computing to the edge systems.
Cross-Layer Strategic Ensemble Defense Against Adversarial Examples
Deep neural network (DNN) has demonstrated its success in multiple domains.
Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness
In this paper we first give an overview of the concept of ensemble diversity and examine the three types of ensemble diversity in the context of DNN classifiers.
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks
Deep neural networks (DNNs) have demonstrated impressive performance on many challenging machine learning tasks.
Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural Networks
Learning Rate (LR) is an important hyper-parameter to tune for effective training of deep neural networks (DNNs).
DA-LSTM: A Long Short-Term Memory with Depth Adaptive to Non-uniform Information Flow in Sequential Data
In this paper, we develop a Depth-Adaptive Long Short-Term Memory (DA-LSTM) architecture, which can dynamically adjust the structure depending on information distribution without prior knowledge.
Representation Learning of Pedestrian Trajectories Using Actor-Critic Sequence-to-Sequence Autoencoder
Representation learning of pedestrian trajectories transforms variable-length timestamp-coordinate tuples of a trajectory into a fixed-length vector representation that summarizes spatiotemporal characteristics.
