Search Results for author:
Found 10 papers, 4 papers with code
NNSplitter: An Active Defense Solution to DNN Model via Automated Weight Obfuscation
As a type of valuable intellectual property (IP), deep neural network (DNN) models have been protected by techniques like watermarking.
MetaLDC: Meta Learning of Low-Dimensional Computing Classifiers for Fast On-Device Adaption
Fast model updates for unseen tasks on intelligent edge devices are crucial but also challenging due to the limited computational power.
RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference
The proliferation of deep learning (DL) has led to the emergence of privacy and security concerns.
PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference
The rapid growth and deployment of deep learning (DL) has witnessed emerging privacy and security concerns.
SDFE-LV: A Large-Scale, Multi-Source, and Unconstrained Database for Spotting Dynamic Facial Expressions in Long Videos
In this paper, we present a large-scale, multi-source, and unconstrained database called SDFE-LV for spotting the onset and offset frames of a complete dynamic facial expression from long videos, which is known as the topic of dynamic facial expression spotting (DFES) and a vital prior step for lots of facial expression analysis tasks.
ObfuNAS: A Neural Architecture Search-based DNN Obfuscation Approach
Nonetheless, we observe that, with only extracting an obfuscated DNN architecture, the adversary can still retrain a substitute model with high performance (e. g., accuracy), rendering the obfuscation techniques ineffective.
LeHDC: Learning-Based Hyperdimensional Computing Classifier
Thanks to the tiny storage and efficient execution, hyperdimensional Computing (HDC) is emerging as a lightweight learning framework on resource-constrained hardware.
A Brain-Inspired Low-Dimensional Computing Classifier for Inference on Tiny Devices
Nonetheless, they have two fundamental drawbacks, heuristic training process and ultra-high dimension, which result in sub-optimal inference accuracy and large model sizes beyond the capability of tiny devices with stringent resource constraints.
Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGA
Specifically, she can aggressively overload the shared power distribution system of FPGA with malicious power-plundering circuits, achieving adversarial weight duplication (AWD) hardware attack that duplicates certain DNN weight packages during data transmission between off-chip memory and on-chip buffer, to hijack the DNN function of the victim tenant.
EOP: An Encryption-Obfuscation Solution for Protecting PCBs Against Tampering and Reverse Engineering
These modules guarantee the stream cipher is correctly synchronized and free from tampering.
Cryptography and Security
