LightPure: Realtime Adversarial Image Purification for Mobile Devices Using Diffusion Models
Hossein Khalili, Seongbin Park, Vincent Li, Brandan Bright, Ali, Payani, Ramana Rao Kompella, Nader Sehatbakhsh

TL;DR
LightPure is a novel adversarial image purification method that significantly improves speed and accuracy for mobile devices using a diffusion and GAN framework, enabling robust real-time defense against attacks.
Contribution
It introduces a two-step diffusion and one-shot GAN framework that enhances adversarial purification efficiency and accuracy specifically for resource-constrained mobile systems.
Findings
Outperforms existing methods by up to 10x in latency
Achieves higher accuracy and robustness against attacks
Demonstrated on Jetson Nano with various datasets
Abstract
Autonomous mobile systems increasingly rely on deep neural networks for perception and decision-making. While effective, these systems are vulnerable to adversarial machine learning attacks where minor input perturbations can significantly impact outcomes. Common countermeasures involve adversarial training and/or data or network transformation. These methods, though effective, require full access to typically proprietary classifiers and are costly for large models. Recent solutions propose purification models, which add a "purification" layer before classification, eliminating the need to modify the classifier directly. Despite their effectiveness, these methods are compute-intensive, making them unsuitable for mobile systems where resources are limited and low latency is essential. This paper introduces LightPure, a new method that enhances adversarial image purification. It…
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Taxonomy
TopicsPhysical Unclonable Functions (PUFs) and Hardware Security · Adversarial Robustness in Machine Learning · Image Processing Techniques and Applications
MethodsDiffusion · SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
