CAGFuzz: Coverage-Guided Adversarial Generative Fuzzing Testing of Deep Learning Systems

Abstract

Deep Learning systems (DL) based on Deep Neural Networks (DNNs) are more and more used in various aspects of our life, including unmanned vehicles, speech processing, and robotics. However, due to the limited dataset and the dependence on manual labeling data, DNNs often fail to detect their erroneous behaviors, which may lead to serious problems. Several approaches have been proposed to enhance the input examples for testing DL systems. However, they have the following limitations. First, they design and generate adversarial examples from the perspective of model, which may cause low generalization ability when they are applied to other models. Second, they only use surface feature constraints to judge the difference between the adversarial example generated and the original example. The deep feature constraints, which contain high-level semantic information, such as image object category and scene semantics are completely neglected. To address these two problems, in this paper, we propose CAGFuzz, a Coverage-guided Adversarial Generative Fuzzing testing approach, which generates adversarial examples for a targeted DNN to discover its potential defects. First, we train an adversarial case generator (AEG) from the perspective of general data set. Second, we extract the depth features of the original and adversarial examples, and constrain the adversarial examples by cosine similarity to ensure that the semantic information of adversarial examples remains unchanged. Finally, we retrain effective adversarial examples to improve neuron testing coverage rate. Based on several popular data sets, we design a set of dedicated experiments to evaluate CAGFuzz. The experimental results show that CAGFuzz can improve the neuron coverage rate, detect hidden errors, and also improve the accuracy of the target DNN.

References in corpus (17)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
• DeepXplore: Automated Whitebox Testing of Deep Learning Systems
• Generating Natural Adversarial Examples
• DLFuzz: Differential Fuzzing Testing of Deep Learning Systems
• PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
• Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models
• Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control
• Testing Deep Neural Networks
• TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
• Machine Learning Testing: Survey, Landscapes and Horizons
• The Insecurity of Home Digital Voice Assistants -- Amazon Alexa as a Case Study
• Combinatorial Testing for Deep Learning Systems
• DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing
• Intriguing Properties of Adversarial Examples
• Software Reliability Growth Models Predict Autonomous Vehicle Disengagement Events

Cited by in corpus (1)

• CoCoFuzzing: Testing Neural Code Models with Coverage-Guided Fuzzing