Time-aware and task-transferable adversarial attack for perception of autonomous vehicles

Adversarial examples: A survey of attacks and defenses in deep learning-enabled cybersecurity systems

Untargeted white-box adversarial attack with heuristic defence methods in real-time deep learning based network intrusion detection system

Some recent articles have revealed that synthetic aperture radar automatic target recognition (SAR-ATR) models based on deep learning are vulnerable to the attacks of adversarial examples and cause security problems. The adversarial attack can make a deep convolutional neural network (CNN)-based SAR-ATR system output the intended wrong label predictions by adding small adversarial perturbations to the SAR images. The existing optimization-based adversarial attack methods generate adversarial examples by minimizing the mean-squared reconstruction error, causing smooth target edge and blurry weak scattering centers in SAR images. In this paper, we build a UNet-generative adversarial network (GAN) to refine the generation of the SAR-ATR models’ adversarial examples. The UNet learns the separable features of the targets and generates the adversarial examples of SAR images. The GAN makes the generated adversarial examples approximate to real SAR images (with sharp target edge and explicit weak scattering centers) and improves the generation efficiency. We carry out abundant experiments using the proposed adversarial attack algorithm to fool the SAR-ATR models based on several advanced CNNs, which are trained on the measured SAR images of the ground vehicle targets. The quantitative and qualitative results demonstrate the high-quality adversarial example generation and excellent attack effectiveness and efficiency improvement.

Recent studies have shown that deep learning models such as image classification and object detection models are vulnerable to attacks from adversarial examples. These malicious examples may trigger potential security threats and cause damage in security-related fields. In this paper, in order to find model robustness weakness, we obtain the adversarial robustness metric against several adversarial attacks, including gradient-based attacks, optimization-based attacks, etc. The metric quantizes the adversarial robustness of deep learning models and can be used as a guide for extra model training. Meanwhile, we design a testing platform to achieve model adversarial robustness evaluation. Utilizing the front and back end separation strategy, the testing platform consist of 2 modules: one is multiple adversarial example attacks executor and the other is HCI module. In addition, the tool allows parallel comparison among different adversarial attack algorithms under given conditions and measures the quality of generated adversarial examples utilizing FID(Fréchet Inception Distance) metrics. Besides, we developed ERFGSM(edge-RFGSM), a new gradient-based attack using edge information through canny operator.

Boosting robustness of network intrusion detection systems: A novel two phase defense strategy against untargeted white-box optimization adversarial attack

Language Models today provide a high accuracy across a large number of\ndownstream tasks. However, they remain susceptible to adversarial attacks,\nparticularly against those where the adversarial examples maintain considerable\nsimilarity to the original text. Given the multilingual nature of text, the\neffectiveness of adversarial examples across translations and how machine\ntranslations can improve the robustness of adversarial examples remain largely\nunexplored. In this paper, we present a comprehensive study on the robustness\nof current text adversarial attacks to round-trip translation. We demonstrate\nthat 6 state-of-the-art text-based adversarial attacks do not maintain their\nefficacy after round-trip translation. Furthermore, we introduce an\nintervention-based solution to this problem, by integrating Machine Translation\ninto the process of adversarial example generation and demonstrating increased\nrobustness to round-trip translation. Our results indicate that finding\nadversarial examples robust to translation can help identify the insufficiency\nof language models that is common across languages, and motivate further\nresearch into multilingual adversarial attacks.\n

Machine learning (ML) provides effective means to learn from spectrum data and solve complex tasks involved in wireless communications. Supported by recent advances in computational resources and algorithmic designs, deep learning (DL) has found success in performing various wireless communication tasks such as signal recognition, spectrum sensing and waveform design. However, ML in general and DL in particular have been found vulnerable to manipulations thus giving rise to a field of study called adversarial machine learning (AML). Although AML has been extensively studied in other data domains such as computer vision and natural language processing, research for AML in the wireless communications domain is still in its early stage. This paper presents a comprehensive review of the latest research efforts focused on AML in wireless communications while accounting for the unique characteristics of wireless systems. First, the background of AML attacks on deep neural networks is discussed and a taxonomy of AML attack types is provided. Various methods of generating adversarial examples and attack mechanisms are also described. In addition, an holistic survey of existing research on AML attacks for various wireless communication problems as well as the corresponding defense mechanisms in the wireless domain are presented. Finally, as new attacks and defense techniques are developed, recent research trends and the overarching future outlook for AML in next-generation wireless communications are discussed.

State-of-the-art neural network models are actively used in various fields, but it is well-known that they are vulnerable to adversarial example attacks. Throughout the efforts to make the models robust against adversarial example attacks, it has been found to be a very difficult task. While many defense approaches were shown to be not effective, adversarial training remains as one of the promising methods. In adversarial training, the training data are augmented by “adversarial” samples generated using an attack algorithm. If the attacker uses a similar attack algorithm to generate adversarial examples, the adversarially trained network can be quite robust to the attack. However, there are numerous ways of creating adversarial examples, and the defender does not know what algorithm the attacker may use. A natural question is: Can we use adversarial training to train a model robust to multiple types of attack? Previous work have shown that, when a network is trained with adversarial examples generated from multiple attack methods, the network is still vulnerable to white-box attacks where the attacker has complete access to the model parameters. In this paper, we study this question in the context of black-box attacks, which can be a more realistic assumption for practical applications. Experiments with the MNIST dataset show that adversarially training a network with an attack method helps defending against that particular attack method, but has limited effect for other attack methods. In addition, even if the defender trains a network with multiple types of adversarial examples and the attacker attacks with one of the methods, the network could lose accuracy to the attack if the attacker uses a different data augmentation strategy on the target network. These results show that it is very difficult to make a robust network using adversarial training, even for black-box settings where the attacker has restricted information on the target network.

Deep neural network-based synthetic aperture radar (SAR) automatic target recognition (ATR) systems are susceptible to attack by adversarial examples, which leads to misclassification by the SAR ATR system, resulting in theoretical model robustness problems and security problems in practice. Inspired by optical images, current SAR ATR adversarial example generation is performed in the image domain. However, the imaging principle of SAR images is based on the imaging of the echo signals interacting between the SAR and objects. Generating adversarial examples only in the image domain cannot change the physical world to achieve adversarial attacks. To solve these problems, this article proposes a framework for generating SAR adversarial examples in a 3D physical scene. First, adversarial attacks are implemented in the 2D image space, and the perturbation in the image space is converted into simulated rays that constitute SAR images through backpropagation optimization methods. The mapping between the simulated rays constituting SAR images and the 3D model is established through coordinate transformation, and point correspondence to triangular faces and intensity values to texture parameters are established. Thus, the simulated rays constituting SAR images are mapped to the 3D model, and the perturbation in the 2D image space is converted back to the 3D physical space to obtain the position and intensity of the perturbation in the 3D physical space, thereby achieving physical adversarial attacks. The experimental results show that our attack method can effectively perform SAR adversarial attacks in the physical world. In the digital world, we achieved an average fooling rate of up to 99.02% for three objects in six classification networks. In the physical world, we achieved an average fooling rate of up to 97.87% for these objects, with a certain degree of transferability across the six different network architectures. To the best of our knowledge, this is the first work to implement physical attacks in a full physical simulation condition. Our research establishes a theoretical foundation for the future concealment of SAR targets in practical settings and offers valuable insights for enhancing the attack and defense capabilities of subsequent DNNs in SAR ATR systems.

Existing research shows that cooperative multi-agent deep reinforcement learning (c-MADRL) is vulnerable to adversarial attacks, and c-MADRL is increasingly being applied to safety-critical domains. However, the robustness of c-MADRL against adversarial attacks has not been fully studied. In the setting of c-MADRL, unlike the single-agent scenario, an adversary can attack multiple agents or all agents at each time step, but the attacker needs more computation to generate adversarial examples and will be more easily detected. Therefore, how the attacker chooses one or several agents instead of all agents to attack is a significant issue in the setting of c-MADRL. Aiming to address this issue, this paper proposes a novel adversarial attack approach, which dynamically groups the agents according to relevant features and selects a group to attack based on the group’s contribution to the overall reward, thus effectively reducing the cost and number of attacks, as well as improving attack efficiency and decreasing the chance of attackers being detected. Moreover, we exploit the transferability of adversarial examples to greatly reduce the computational cost of generating adversarial examples. Our method is tested in multi-agent particle environments (MPE) and in StarCraft II. Experimental results demonstrate that our proposed method can effectively degrade the performance of multi-agent deep reinforcement learning algorithms with fewer attacks and lower computational costs.

Adversarial attacks, particularly targeted transfer-based attacks, can be used to assess the adversarial robustness of large visual-language models (VLMs), allowing for a more thorough examination of potential security flaws before deployment. However, previous transfer-based adversarial attacks incur high costs due to high iteration counts and complex method structure. Furthermore, due to the unnaturalness of adversarial semantics, the generated adversarial examples have low transferability. These issues limit the utility of existing methods for assessing robustness. To address these issues, we propose AdvDiffVLM, which uses diffusion models to generate natural, unrestricted and targeted adversarial examples via score matching. Specifically, AdvDiffVLM uses Adaptive Ensemble Gradient Estimation (AEGE) to modify the score during the diffusion model’s reverse generation process, ensuring that the produced adversarial examples have natural adversarial targeted semantics, which improves their transferability. Simultaneously, to improve the quality of adversarial examples, we use the GradCAM-guided Mask Generation (GCMG) to disperse adversarial semantics throughout the image rather than concentrating them in a single area. Finally, AdvDiffVLM embeds more target semantics into adversarial examples after multiple iterations. Experimental results show that our method generates adversarial examples 5x to 10x faster than state-of-the-art (SOTA) transfer-based adversarial attacks while maintaining higher quality adversarial examples. Furthermore, compared to previous transfer-based adversarial attacks, the adversarial examples generated by our method have better transferability. Notably, AdvDiffVLM can successfully attack a variety of commercial VLMs in a black-box environment, including GPT-4V. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/gq-max/AdvDiffVLM</uri>

The goal of textual adversarial attack methods is to replace some words in an input text in order to make the victim model misbehave. This article proposes an effective word-level adversarial attack method based on sememes and an improved quantum-behaved particle swarm optimization (QPSO) algorithm. The sememe-based substitute method, which uses the words sharing the same sememes as the substitutes of the original words, is first employed to form the reduced search space. Then, an improved QPSO algorithm, called historical information-guided QPSO with random drift local attractor (HIQPSO-RD), is proposed to search the reduced search space for adversarial examples. The HIQPSO-RD introduces historical information into the current mean best position of the QPSO, for the purpose of improving the convergence speed of the algorithm, by enhancing its exploration ability and preventing the premature convergence of the swarm. The proposed algorithm uses the random drift local attractor technique to make a good balance between its exploration and exploitation, so that the algorithm can find a better adversarial attack example with low grammaticality and perplexity (PPL). In addition, it employs a two-stage diversity control strategy to enhance the search performance of the algorithm. Experiments on three natural language processing (NLP) datasets, with three commonly used nature language processing models as victim models, show that our method achieves higher attack success rates but lower modification rates than the state-of-the-art adversarial attack methods. Moreover, the results of human evaluations show that adversarial examples generated by our method can better maintain the semantic similarity and grammatical correctness of the original input.

Deep Neural Networks (DNNs) are vulnerable to adversarial attacks, especially white-box targeted attacks. This paper studies the problem of how aggressive white-box targeted attacks can be to go beyond widely used Top-1 attacks. We propose to learn ordered Top-k attacks (k ≥ 1), which enforce the Top-k predicted labels of an adversarial example to be the k (randomly) selected and ordered labels (the ground-truth label is exclusive). Two methods are presented. First, we extend the vanilla Carlini-Wagner (C&W) method and use it as a strong baseline. Second, we present an Adversarial Distillation (AD) framework consisting of two components: (i) Computing an adversarial probability distribution for a given ordered Top-k targeted labels. (ii) Learning adversarial examples by minimizing the Kullback-Leibler (KL) divergence between the adversarial distribution and the predicted distribution, together with the perturbation energy penalty. In computing adversarial distributions, we explore how to leverage label semantic similarities, leading to knowledge-oriented attacks. In experiments, we test Top-k (k = 1, 2, 5, 10) attacks in the ImageNet-1000 val dataset using three representative DNNs trained with the clean ImageNet-1000 train dataset, ResNet-50 [11], DenseNet-121 [14] and AOGNet-12M [21]. Overall, the proposed AD approach obtains the best results, especially by a large margin when computation budget is limited. It reduces the perturbation energy consistently with the same attack success rate on all the four k's, and improve the attack success rate by a large margin against the modified C&W method for k = 10.

Deep neural networks(DNNs) are widely used in AI-controlled Cyber-Physical Systems (CPS) to controll cars, robotics, water treatment plants and railways. However, DNNs have vulnerabilities to well-designed input samples that are called adversarial examples. Adversary attack is one of the important techniques for detecting and improving the security of neural networks. Existing attacks, including state-of-the-art black-box attack have a lower success rate and make invalid queries that are not beneficial to obtain the direction of generating adversarial examples. For these reasons, this paper proposed a CMA-ES-based adversarial attack on black-box DNNs. Firstly, an efficient method to reduce the number of invalid queries is introduced. Secondly, a black-box attack of generating adversarial examples to fit a high-dimensional independent Gaussian distribution of the local solution space is proposed. Finally, a new CMA-based perturbation compression method is applied to make the process of reducing perturbation smoother. Experimental results on ImageNet classifiers show that the proposed attack has a higher success-rate than the state-of-the-art black-box attack but reduce the number of queries by 30% equally.

Explanation-Guided Adversarial Example Attacks