MULDEF: Multi-model-based Defense Against Adversarial Examples for Neural Networks

Abstract

Despite being popularly used in many applications, neural network models have been found to be vulnerable to adversarial examples, i.e., carefully crafted examples aiming to mislead machine learning models. Adversarial examples can pose potential risks on safety and security critical applications. However, existing defense approaches are still vulnerable to attacks, especially in a white-box attack scenario. To address this issue, we propose a new defense approach, named MulDef, based on robustness diversity. Our approach consists of (1) a general defense framework based on multiple models and (2) a technique for generating these multiple models to achieve high defense capability. In particular, given a target model, our framework includes multiple models (constructed from the target model) to form a model family. The model family is designed to achieve robustness diversity (i.e., an adversarial example successfully attacking one model cannot succeed in attacking other models in the family). At runtime, a model is randomly selected from the family to be applied on each input example. Our general framework can inspire rich future research to construct a desirable model family achieving higher robustness diversity. Our evaluation results show that MulDef (with only up to 5 models in the family) can substantially improve the target model's accuracy on adversarial examples by 22-74% in a white-box attack scenario, while maintaining similar accuracy on legitimate examples.

References in corpus (11)

• Ensemble Adversarial Training: Attacks and Defenses
• Conditional Image Generation with PixelCNN Decoders
• Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
• Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
• DeepXplore: Automated Whitebox Testing of Deep Learning Systems
• Delving into Transferable Adversarial Examples and Black-box Attacks
• Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
• Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
• Defensive Distillation is Not Robust to Adversarial Examples
• Simple Black-Box Adversarial Perturbations for Deep Networks
• Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers

Cited by in corpus (7)

• Deep Learning for Anomaly Detection: A Survey
• Software Engineering for AI-Based Systems: A Survey
• Increasing the Confidence of Deep Neural Networks by Coverage Analysis
• On the Certified Robustness for Ensemble Models and Beyond
• Pelta: Shielding Transformers to Mitigate Evasion Attacks in Federated Learning
• BUZz: BUffer Zones for defending adversarial examples in image classification
• Robust Deep Learning Ensemble against Deception