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Found 17 papers, 1 papers with code
ActPerFL: Active Personalized Federated Learning
Inspired by Bayesian hierarchical models, we develop ActPerFL, a self-aware personalized FL method where each client can automatically balance the training of its local personal model and the global model that implicitly contributes to other clients’ training.
Comparative Analysis of Deep Learning Models for Brand Logo Classification in Real-World Scenarios
This report presents a comprehensive study on deep learning models for brand logo classification in real-world scenarios.
Joint Engagement Classification using Video Augmentation Techniques for Multi-person Human-robot Interaction
This work serves as the first step toward fully unlocking the potential of end-to-end video understanding models pre-trained on large public datasets and augmented with data augmentation and visualization techniques for affect recognition in the multi-person human-robot interaction in the wild.
PerD: Perturbation Sensitivity-based Neural Trojan Detection Framework on NLP Applications
Deep Neural Networks (DNNs) have been shown to be susceptible to Trojan attacks.
Self-Aware Personalized Federated Learning
In the context of personalized federated learning (FL), the critical challenge is to balance local model improvement and global model tuning when the personal and global objectives may not be exactly aligned.
AdaTest:Reinforcement Learning and Adaptive Sampling for On-chip Hardware Trojan Detection
This paper proposes AdaTest, a novel adaptive test pattern generation framework for efficient and reliable Hardware Trojan (HT) detection.
An Adaptive Black-box Backdoor Detection Method for Deep Neural Networks
Deep Neural Networks (DNNs) have demonstrated unprecedented performance across various fields such as medical diagnosis and autonomous driving.
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection
The goal of federated learning (FL) is to train one global model by aggregating model parameters updated independently on edge devices without accessing users' private data.
ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning
When extended to new vulnerability types, ESCORT yields an average F1-score of 93%.
TAD: Trigger Approximation based Black-box Trojan Detection for AI
While DNNs are widely employed in security-sensitive fields, they are identified to be vulnerable to Neural Trojan (NT) attacks that are controlled and activated by the stealthy trigger.
ProFlip: Targeted Trojan Attack With Progressive Bit Flips
In this work, we present ProFlip, the first targeted Trojan attack framework that can divert the prediction of the DNN to the target class by progressively identifying and flipping a small set of bits in model parameters.
Dyadic Speech-based Affect Recognition using DAMI-P2C Parent-child Multimodal Interaction Dataset
Automatic speech-based affect recognition of individuals in dyadic conversation is a challenging task, in part because of its heavy reliance on manual pre-processing.
Trustworthy AI Inference Systems: An Industry Research View
Additionally, such systems should also use Privacy-Enhancing Technologies (PETs) to protect customers' data at any time.
Coda: An End-to-End Neural Program Decompiler
Furthermore, Coda outperforms the sequence-to-sequence model with attention by a margin of 70% program accuracy.
A Neural-based Program Decompiler
Reverse engineering of binary executables is a critical problem in the computer security domain.
BlackMarks: Black-box Multi-bit Watermarking for Deep Neural Networks
To extract the WM, BlackMarks queries the model with the WM key images and decodes the owner’s signature from the corresponding predictions using the designed encoding scheme.
DeepSigns: A Generic Watermarking Framework for IP Protection of Deep Learning Models
The resulting models are therefore considered to be the IP of the model builder and need to be protected to preserve the owner's competitive advantage.
Cryptography and Security
