Dormant Neural Trojans

11/02/2022
by   Feisi Fu, et al.
0

We present a novel methodology for neural network backdoor attacks. Unlike existing training-time attacks where the Trojaned network would respond to the Trojan trigger after training, our approach inserts a Trojan that will remain dormant until it is activated. The activation is realized through a specific perturbation to the network's weight parameters only known to the attacker. Our analysis and the experimental results demonstrate that dormant Trojaned networks can effectively evade detection by state-of-the-art backdoor detection methods.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/15/2023

OVLA: Neural Network Ownership Verification using Latent Watermarks

Ownership verification for neural networks is important for protecting t...
research
06/10/2020

Scalable Backdoor Detection in Neural Networks

Recently, it has been shown that deep learning models are vulnerable to ...
research
04/27/2022

Detecting Backdoor Poisoning Attacks on Deep Neural Networks by Heatmap Clustering

Predicitions made by neural networks can be fraudulently altered by so-c...
research
09/03/2022

Phishing URL Detection: A Network-based Approach Robust to Evasion

Many cyberattacks start with disseminating phishing URLs. When clicking ...
research
08/08/2023

Improved Activation Clipping for Universal Backdoor Mitigation and Test-Time Detection

Deep neural networks are vulnerable to backdoor attacks (Trojans), where...
research
12/18/2020

Robustness of Facial Recognition to GAN-based Face-morphing Attacks

Face-morphing attacks have been a cause for concern for a number of year...
research
05/28/2019

Attacker Behaviour Profiling using Stochastic Ensemble of Hidden Markov Models

Cyber threat intelligence is one of the emerging areas of focus in infor...

Please sign up or login with your details

Forgot password? Click here to reset