DeepAI AI Chat
Log In Sign Up

Real-time Detection of Practical Universal Adversarial Perturbations

by   CK, et al.

Universal Adversarial Perturbations (UAPs) are a prominent class of adversarial examples that exploit the systemic vulnerabilities and enable physically realizable and robust attacks against Deep Neural Networks (DNNs). UAPs generalize across many different inputs; this leads to realistic and effective attacks that can be applied at scale. In this paper we propose HyperNeuron, an efficient and scalable algorithm that allows for the real-time detection of UAPs by identifying suspicious neuron hyper-activations. Our results show the effectiveness of HyperNeuron on multiple tasks (image classification, object detection), against a wide variety of universal attacks, and in realistic scenarios, like perceptual ad-blocking and adversarial patches. HyperNeuron is able to simultaneously detect both adversarial mask and patch UAPs with comparable or better performance than existing UAP defenses whilst introducing a significantly reduced latency of only 0.86 milliseconds per image. This suggests that many realistic and practical universal attacks can be reliably mitigated in real-time, which shows promise for the robust deployment of machine learning systems.


Jacobian Regularization for Mitigating Universal Adversarial Perturbations

Universal Adversarial Perturbations (UAPs) are input perturbations that ...

Turning Your Strength against You: Detecting and Mitigating Robust and Universal Adversarial Patch Attack

Adversarial patch attack against image classification deep neural networ...

Universal Adversarial Perturbations: A Survey

Over the past decade, Deep Learning has emerged as a useful and efficien...

Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses

This paper focuses on learning transferable adversarial examples specifi...

SLAP: Improving Physical Adversarial Examples with Short-Lived Adversarial Perturbations

Whilst significant research effort into adversarial examples (AE) has em...

Adversarial Perturbations Against Real-Time Video Classification Systems

Recent research has demonstrated the brittleness of machine learning sys...

Resilience of Autonomous Vehicle Object Category Detection to Universal Adversarial Perturbations

Due to the vulnerability of deep neural networks to adversarial examples...