Deep neural network (DNN) models are susceptible to adversarial samples in white-box and opaque environments. Although previous studies have shown high attack success rates, coupling DNN models with interpretation models could offer a sense of security when a human expert is involved. However, in white-box environments, interpretable deep learning systems (IDLSes) have been shown to be vulnerable to malicious manipulations. As access to the components of IDLSes is limited in opaque settings, it becomes more challenging for the adversary to fool the system. In this work, we propose aQuery-efficientScore-based opaque attack against IDLSes, which requires no knowledge of the target model and its coupled interpretation model. By continuously refining the adversarial samples created based on feedback scores from the IDLS, our approach effectively reduces the number of model queries and navigates the search space to identify perturbations that can fool the system. We evaluate the attack's effectiveness on four convolutional neural network (CNN) models and two interpretation models, using both ImageNet and CIFAR datasets. Our results show that the proposed approach is query-efficient with a high attack success rate that can reach more than 95%, and an average transferability success rate of 69%. We have also demonstrated that our attack is resilient against various preprocessing defense techniques.

Deep learning methods have gained increasing attention in various applications due to their outstanding performance. For exploring how this high performance relates to the proper use of data artifacts and the accurate problem formulation of a given task, interpretation models have become a crucial component in developing deep learning-based systems. Interpretation models enable the understanding of the inner workings of deep learning models and offer a sense of security in detecting the misuse of artifacts in the input data. Similar to prediction models, interpretation models are also susceptible to adversarial inputs. This work introduces two attacks, AdvEdge and AdvEdge$^{+}$, which deceive both the target deep learning model and the coupled interpretation model. We assess the effectiveness of proposed attacks against four deep learning model architectures coupled with four interpretation models that represent different categories of interpretation models. Our experiments include the implementation of attacks using various attack frameworks. We also explore the attack resilience against three general defense mechanisms and potential countermeasures. Our analysis shows the effectiveness of our attacks in terms of deceiving the deep learning models and their interpreters, and highlights insights to improve and circumvent the attacks.

Establishing trust and helping experts debug and understand the inner workings of deep learning models, interpretation methods are increasingly coupled with these models, building interpretable deep learning systems. However, adversarial attacks pose a significant threat to public trust by making interpretations of deep learning models confusing and difficult to understand. In this paper, we present a novel Single-class target-specific ADVersarial attack called SingleADV. The goal of SingleADV is to generate a universal perturbation that deceives the target model into confusing a specific category of objects with a target category while ensuring highly relevant and accurate interpretations. The universal perturbation is stochastically and iteratively optimized by minimizing the adversarial loss that is designed to consider both the classifier and interpreter costs in targeted and non-targeted categories. In this optimization framework, ruled by the first- and second-moment estimations, the desired loss surface promotes high confidence and interpretation scores of adversarial samples. By avoiding unintended misclassification of samples from other categories, SingleADV enables more effective targeted attacks on interpretable deep learning systems in both white-box and black-box scenarios. To evaluate the effectiveness of SingleADV, we conduct experiments using four different model architectures (ResNet-50, VGG-16, DenseNet-169, and Inception-V3) coupled with three interpretation models (CAM, Grad, and MASK). Through extensive empirical evaluation, we demonstrate that SingleADV effectively deceives target deep learning models and their associated interpreters under various conditions and settings. Our results show that the performance of SingleADV is effective, with an average attack success rate of 74% and prediction confidence exceeding 77% on successful adversarial samples. Furthermore, we discuss several countermeasures against SingleADV, including a transfer-based learning approach and existing preprocessing defenses.

Deep neural network models are susceptible to malicious manipulations even in the black-box settings. Providing explanations for DNN models offers a sense of security by human involvement, which reveals whether the sample is benign or adversarial even though previous studies achieved a high attack success rate. However, interpretable deep learning systems (IDLSes) are shown to be susceptible to adversarial manipulations in white-box settings. Attacking IDLSes in black-box settings is challenging and remains an open research domain. In this work, we propose a black-box version of the white-box AdvEdge approach against IDLSes, which is query-efficient and gradient-free without obtaining any knowledge of the target DNN model and its coupled interpreter. Our approach takes advantage of transfer-based and score-based techniques using the effective microbial genetic algorithm (MGA). We achieve a high attack success rate with a small number of queries and high similarity in interpretations between adversarial and benign samples.

Deep Learning is rapidly evolving to the point that it can be used in crucial safety and security applications, including self-driving vehicles, surveillance, drones, and robots. However, these deep learning models are vulnerable to attacks based on adversarial samples that are undetectable to the human eye but cause the model to misbehave. There is an increasing demand for comprehensive and in-depth analysis of behaviors of various attacks and the possible defenses against common deep learning models under several adversarial scenarios. In this study, we conducted four separate investigations. First, we examine the relationship between the model's complexity and its robustness against the studied attacks. Second, the connection between the performance and diversity of models is examined. Third, the first and second experiments were tested across different datasets to explore the impact of the dataset on the performance of the model. Four, throughout the defense strategies, the model behavior is extensively investigated. The code, trained models, and detailed settings and results are available at: https://github.com/InfoLab-SKKU/ML-Adversarial-Attacks-Analysis

The rapid pace of malware development and the widespread use of code obfuscation, polymorphism, and morphing techniques pose a considerable challenge to detecting and analyzing malware. Today, it is difficult for antivirus applications to use traditional signature-based detection methods to detect morphing malware. Thus, the emergence of structure graph-based detection methods has become a hope to solve this challenge. In this work, we propose a method for detecting malware using graphs' spectral heat and wave signatures, which are efficient and size- and permutation-invariant. We extracted 250 and 1,000 heat and wave representations, and we trained and tested heat and wave representations on eight machine learning classifiers. We used a dataset of 37,537 unpacked Windows malware executables and extracted the control flow graph (CFG) of each windows malware to obtain the spectral representations. Our experimental results showed that by using heat and wave spectral graph theory, the best malware analysis accuracy reached 95.9%.