DOME-T: adversarial computer vision attack on deep learning models based on Tchebichef image moments

Deep neural networks, despite their remarkable performance on image classification tasks, remain critically vulnerable to adversarial examples — imperceptibly perturbed inputs engineered to induce misclassification. In this paper, we propose and evaluate a dual-layer defense framework against adversarial attacks on image classifiers trained on the ImageNet1K Mini dataset. Our approach combines a convolutional autoencoder as a preprocessing denoising step with a ResNet-50 classifier augmented by a novel block-switching mechanism to disrupt adversarial gradient signal. We evaluate the system under Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks across a range of perturbation budgets ε ∈ {0.01, 0.02, 0.03, 0.05, 0.07, 0.10}. Our defense recovers +23.08% accuracy against FGSM and +54.44% against PGD on in-distribution data, and achieves 100% full recovery on an out- of-distribution generalisation test (83.3% baseline). We further employ Gradient-weighted Class Activation Mapping (GRAD- CAM) to visually explain the disruption of model attention under attack and its restoration after defense. All experiments are conducted on the Kaggle T4 GPU platform, and the full pipeline is deployed as an interactive Streamlit web application.

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

Deep learning has revolutionized image recognition. One significant obstacle still remains, the vulnerability of these models to adversarial attacks. These attacks manipulate images with subtle changes that cause CNN misclassification. While methods, such as adversarial training, have been proposed to defend against adversarial attacks, they incur additional training costs, either extra input samples or auxiliary models. In this work, we propose an efficient approach to deploying robust models that utilize image transformations to remove adversarial noises. We investigate the performance of simple transformations and report several effective ones, including Affine blur, Gaussian blur, Median blur, and Bilateral blur against various adversarial attack methods, such as Fast Gradient Sign Method (FGSM), Randomized + FGSM (RFGSM), and Projected Gradient Descent (PGD). We apply these image transformation techniques to the widely used ImageNet dataset, and experimental results demonstrate the potential of image transformation methods as a strong defense against adversarial attacks in deep learning-based image classification systems, especially when combined with cutting-edge neural network architectures such as ResNet50 and DenseNet121. Our comprehensive results show that these transformations can significantly improve the robustness of CNN models against adversarial attacks on ImageNet, achieving a recovery rate of up to 85% to 90% without incurring extra resource costs.

Deep learning architectures have emerged as powerful function approximators in a broad spectrum of complex representation learning tasks, such as, computer vision, natural language processing and collaborative filtering. These architectures bear a high potential to learn the intrinsic structure of data and extract valuable insights. Despite the surge in the development of state-of-the-art intelligent systems using the deep neural networks (DNNs), these systems have found to be vulnerable to adversarial examples produced by adding a small-magnitude of perturbations. Such adversarial examples are adept at misleading the DNN classifiers. In the past, different attack strategies have been proposed to produce adversarial examples in the digital, physical, and transform domain, but the likelihood to generate perceptually realistic adversarial examples require more research efforts. In this paper, we present a novel approach to produce adversarial examples by combining the single-shot fast gradient sign method (FGSM) and spatial, as well as, transform domain image processing techniques. The resulted perturbations neutralize the impact of low-intensity based regions, thus, instilling the noise only in the selective high-intensity regions of the input image. While combining the customized perturbation with one-step FGSM perturbation in an un-targeted black-box attack scenario, the proposed approach successfully fools state-of-the-art DNN classifiers with 99% adversarial examples being misclassified on the ImageNet validation dataset.

This chapter introduces the concept of adversarial attacks on image classification models built on convolutional neural networks (CNN). CNNs are very popular deep-learning models which are used in image classification tasks. However, very powerful and pre-trained CNN models working very accurately on image datasets for image classification tasks may perform disastrously when the networks are under adversarial attacks. In this work, two very well-known adversarial attacks are discussed and their impact on the performance of image classifiers is analyzed. These two adversarial attacks are the fast gradient sign method (FGSM) and adversarial patch attack. These attacks are launched on three powerful pre-trained image classifier architectures, ResNet-34, GoogleNet, and DenseNet-161. The classification accuracy of the models in the absence and presence of the two attacks are computed on images from the publicly accessible ImageNet dataset. The results are analyzed to evaluate the impact of the attacks on the image classification task.

FE-DaST: Fast and effective data-free substitute training for black-box adversarial attacks

Deep learning, particularly convolutional neural networks (CNNs), has proven valuable for brain tumor classification, aiding diagnostic and therapeutic decisions in medical imaging. Despite their accuracy, these models are vulnerable to adversarial attacks, compromising their reliability in clinical settings. In this research, we utilized a VGG16-based CNN model to classify brain tumors, achieving 96% accuracy on clean magnetic resonance imaging (MRI) data. To assess robustness, we exposed the model to Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks, which reduced accuracy to 32% and 13%, respectively. We then applied a multi-layered defense strategy, including adversarial training with FGSM and PGD examples and feature squeezing techniques such as bit-depth reduction and Gaussian blurring. This approach improved model resilience, achieving 54% accuracy on FGSM and 47% on PGD adversarial examples. Our results highlight the importance of proactive defense strategies for maintaining the reliability of AI in medical imaging under adversarial conditions.

Millimeter wave MIMO wireless communication systems are deployed in 5G and next‐generation networks. The effectiveness of deep learning models for improving the performance of these systems has been proven in the literature. However, several deep learning models are vulnerable to security threats, such as adversarial attacks. Therefore, for the deployment of these systems, it is essential to make them resilient to such kinds of attacks for good quality secure communication. Adversarial training is a solution by which deep learning models are trained for adversarial attacks beforehand. Adversarial training for three types of adversarial attacks, that is, Fast Gradient Sign Method, Iterative Fast Gradient Sign Method, and Momentum Iterative Fast Gradient Sign Method is implemented in this paper. The simulation results depict a decrease in the error at the receiving end after adversarial training, even after an adversarial attack has been applied. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Although deep neural networks (DNNs) have achieved high performance across various applications, they are often deceived by adversarial examples generated by adding small perturbations. To combat adversarial attacks, many detection methods have been proposed, including feature squeezing and trapdoor. However, these methods rely on the output of DNNs or involve training a separate network to detect adversarial examples, which leads to high computational costs and low efficiency. In this study, we propose a simple and effective approach called the entropy-based detector (EBD) to protect DNNs from various adversarial attacks. EBD detects adversarial examples by comparing the difference in entropy between the input sample before and after bit depth reduction. We show that EBD can detect over 98% of the adversarial examples generated by attacks using fast-gradient sign method, basic iterative method, momentum iterative method, DeepFool and CW attacks when the false positive rate is 2.5% for CIFAR-10 and ImageNet datasets.

Adversarial attacks aim to deceive the target system. Recently, deep learning methods have become a target of adversarial attacks. Even small perturbations could lead to classification errors in deep learning models. In an intrusion detection system using deep learning method, adversarial attack can cause classification error and malicious traffic can be classified as benign. In this study, the effects of adversarial attacks on the accuracy of deep learning-based intrusion detection systems were examined. CICIDS2017 dataset was used to test the detection systems . At first, DDoS attacks weredetected using Autoencoder, MLP, AEMLP, DNN, AEDNN, CNN and AECNN methods. Then, the Fast Gradient Sign Method (FGSM) is used to perform adversarial attacks. Finally, the sensitivity of the methods against the adversarial attacks were examined. Our results showed that the classification performance of deep learning based detection methods decreased up to %17 after the adversarial attacks. The results obtained in this study form the basis for the validation and validation studies of learning-based intrusion detection systems.

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.

The successful emergence of deep learning (DL) in wireless system applications has raised concerns about new security-related challenges. One such security challenge is adversarial attacks. Although there has been much work demonstrating the susceptibility of DL-based classification tasks to adversarial attacks, regression-based problems in the context of a wireless system have not been studied so far from an attack perspective. The aim of this paper is twofold: (i) we consider a regression problem in a wireless setting and show that adversarial attacks can break the DL-based approach and (ii) we analyze the effectiveness of adversarial training as a defensive technique in adversarial settings and show that the robustness of DL-based wireless system against attacks improves significantly. Specifically, the wireless application considered in this paper is the DL-based power allocation in the downlink of a multicell massive multi-input-multi-output system, where the goal of the attack is to yield an infeasible solution by the DL model. We extend the gradient-based adversarial attacks: fast gradient sign method (FGSM), momentum iterative FGSM, and projected gradient descent method to analyze the susceptibility of the considered wireless application with and without adversarial training. We analyze the deep neural network (DNN) models performance against these attacks, where the adversarial perturbations are crafted using both the white-box and black-box attacks.

In this paper, we propose a new method for detecting adversarial attacks on deep neural networks. Our algorithm is based on the intuition that attacking input images results in different displacement vectors for clean and adversarial classes. For example, if the input image is an adversarial example, the re-attacking process results in a displacement vector with a short length in the feature space, but this displacement is considerable for clean images. We train our detector based on these displacement vectors. The experimental results show that compared to the current learning-based adversarial detection methods, the proposed system is capable of detecting the adversarial examples using a far simpler network. In addition, the proposed method is independent of the attack type, and is able to detect even novel attacks. It is also revealed that the proposed system learns the discrimination function even using a small amount of training data without any hyper-parameter tuning. We obtain remarkable results in detecting adversarial examples which are placed near and far from the decision boundary, improving state-of-the-art in detecting 2-norm Carlini and Wagner attack (L2-C&W) and ∞-norm Projected Gradient Descent attack (L∞-PGD), where just Fast Gradient Sign Method (FGSM) is used for training the system.

Automatic modulation classification (AMC) using the Deep Neural Network (DNN) approach outperforms the traditional classification techniques, even in the presence of challenging wireless channel environments. However, the adversarial attacks cause the loss of accuracy for the DNN-based AMC by injecting a well-designed perturbation to the wireless channels. In this paper, we propose a novel generative adversarial network (GAN)-based countermeasure approach to safeguard the DNN-based AMC systems against adversarial attack examples. GAN-based aims to eliminate the adversarial attack examples before feeding to the DNN-based classifier. Specifically, we have shown the resiliency of our proposed defense GAN against the Fast-Gradient Sign method (FGSM) algorithm as one of the most potent kinds of attack algorithms to craft the perturbed signals. The existing defense-GAN has been designed for image classification and does not work in our case where the above-mentioned communication system is considered. Thus, our proposed countermeasure approach deploys GANs with a mixture of generators to overcome the mode collapsing problem in a typical GAN facing radio signal classification problem. Simulation results show the effectiveness of our proposed defense GAN so that it could enhance the accuracy of the DNN-based AMC under adversarial attacks to 81%, approximately.

Deep learning models have shown their great capability for the hyperspectral image (HSI) classification task in recent years. Nevertheless, their vulnerability towards adversarial attacks could not be neglected. In this study, we systematically analyze the influence of adversarial attacks on the HSI classification task for the first time. While existing research of adversarial attacks focuses on the generation of adversarial examples in the RGB domain, the experiments in this study show such adversarial examples could also exist in the hyperspectral domain. Although the difference between the generated adversarial image and the original hyperspectral data is imperceptible to the human visual system, most of the existing state-of-the-art deep learning models could be fooled by the adversarial image to make wrong predictions. To address this challenge, a novel self-attention context network (SACNet) is further proposed. We discover that the global context information contained in HSI can significantly improve the robustness of deep neural networks when confronted with adversarial attacks. Extensive experiments on three benchmark HSI datasets demonstrate that the proposed SACNet possesses stronger resistibility towards adversarial examples compared with the existing state-of-the-art deep learning models.