CAGFuzz: Coverage-Guided Adversarial Generative Fuzzing Testing for Image-Based Deep Learning Systems

Abstract

Deep Neural Network (DNN) driven technologies have been extensively employed in various aspects of our life. Nevertheless, the applied DNN always fails to detect erroneous behaviors, which may lead to serious problems. Several approaches have been proposed to enhance adversarial examples for automatically testing deep learning (DL) systems, such as image-based DL systems. However, the approaches contain the following two limitations. First, existing approaches only take into account small perturbations on adversarial examples, they design and generate adversarial examples for a certain particular DNN model. This might hamper the transferability of the examples for other DNN models. Second, they only use shallow features (e.g., pixel-level features) to judge the differences between the generated adversarial examples and the original examples. The deep features, which contain high-level semantic information, such as image object categories and scene semantics, are completely neglected. To address these two problems, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CAGFuzz</i> , a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> overage-guided <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> dversarial <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> enerative <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fuzz</u> ing testing approach for image-based DL systems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CAGFuzz</i> is able to generate adversarial examples for mainstream DNN models to discover their potential errors. First, we train an Adversarial Example Generator ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AEG</i> ) based on general datasets. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AEG</i> only considers the data characteristics to alleviate the transferability problem. Second, we extract the deep features of the original and adversarial examples, and constrain the adversarial examples by cosine similarity to ensure that the deep features of the adversarial examples remain unchanged. Finally, we use the adversarial examples to retrain the models. Based on several standard datasets, we design a set of dedicated experiments to evaluate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CAGFuzz</i> . The experimental results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CAGFuzz</i> can detect more hidden errors, enhance the accuracy of the target DNN models, and generate adversarial examples with higher transferability.