activity
20182026
most citedSNIFF: Reverse Engineering of Neural Networks with Fault Attacks

50 citations · 100 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CR2026

A Constant-Time Implementation Methodology for Activation Functions on Microcontrollers

Andrii Tyvodar, Andreas Rechberger, Dirmanto Jap +4

Embedded neural-network inference can leak information through timing side channels, including leakage caused by the evaluation of activation functions. This work proposes a consta…

cs.CR2024★ 1 cited

Side-Channel Analysis of OpenVINO-based Neural Network Models

Dirmanto Jap, Jakub Breier, Zdenko Lehocký +2

Embedded devices with neural network accelerators offer great versatility for their users, reducing the need to use cloud-based services. At the same time, they introduce new secur…

cs.CR2023

A Desynchronization-Based Countermeasure Against Side-Channel Analysis of Neural Networks

Jakub Breier, Dirmanto Jap, Xiaolu Hou +1

Model extraction attacks have been widely applied, which can normally be used to recover confidential parameters of neural networks for multiple layers. Recently, side-channel anal…

cs.CR2021

DeepFreeze: Cold Boot Attacks and High Fidelity Model Recovery on Commercial EdgeML Device

Yoo-Seung Won, Soham Chatterjee, Dirmanto Jap +2

EdgeML accelerators like Intel Neural Compute Stick 2 (NCS) can enable efficient edge-based inference with complex pre-trained models. The models are loaded in the host (like Raspb…

cs.CR2020★ 50 cited

SNIFF: Reverse Engineering of Neural Networks with Fault Attacks

Jakub Breier, Dirmanto Jap, Xiaolu Hou +2

Neural networks have been shown to be vulnerable against fault injection attacks. These attacks change the physical behavior of the device during the computation, resulting in a ch…

cs.LG2019★ 6 cited

Enhancing Fault Tolerance of Neural Networks for Security-Critical Applications

Manaar Alam, Arnab Bag, Debapriya Basu Roy +4

Neural Networks (NN) have recently emerged as backbone of several sensitive applications like automobile, medical image, security, etc. NNs inherently offer Partial Fault Tolerance…

cs.CR2018★ 26 cited

CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information

Lejla Batina, Shivam Bhasin, Dirmanto Jap +1

Machine learning has become mainstream across industries. Numerous examples proved the validity of it for security applications. In this work, we investigate how to reverse enginee…

cs.CR2018★ 17 cited

DeepLaser: Practical Fault Attack on Deep Neural Networks

Jakub Breier, Xiaolu Hou, Dirmanto Jap +3

As deep learning systems are widely adopted in safety- and security-critical applications, such as autonomous vehicles, banking systems, etc., malicious faults and attacks become a…