A new method is proposed, the"Binary Iterative Method"(BinIM), which uses a divide-and-conquer paradigm to optimise parameters and hyper-parameters for the generation of non-targeted attacks.
Abstract
Adversarial attacks guide and provide additional training and test data for both adversarial training and adversarial robustness validation, and expose the'piecewise linearity'of deep learning based models. Since adversarial attacks and adversarial robustness are mathematically defined problems that can be optimised directly with end-to-end differentiable search, adversarial robustness is more widely applicable than other robustness metrics such as corruption and perturbation robustness, and new kinds of adversarial attacks are beneficial for robustness testing. Attacks are targeted or non-targeted depending on whether the image is modified to misclassify to a particular class or to any incorrect class; we focus on the non-targeted setting. Finding the optimal input data points and hyper-parameters for generating non-targeted adversarial attacks remains a challenge for current methods like the Fast Gradient Method, Basic Iterative Method and Virtual Adversarial Method. We propose a new method, the"Binary Iterative Method"(BinIM), which uses a divide-and-conquer paradigm to optimise parameters and hyper-parameters for the generation of non-targeted attacks. We compare our method to other gradient-based adversarial attacks evaluated over pre-trained networks (InceptionV3, InceptionV2, ResNet V2 152) on classification tasks. On 1000 randomly-sampled images from the standard ImageNet dataset, the Binary Iterative Method outperforms all other gradient-based methods, qualitatively making the classifier misclassify with confidence up to 0.995 while reducing the probability of the true label to 2.21e-09 (approximately 0).
The study systematically compares two distinct adversarial training strategies: ‘pre-train’, where adversarial examples are generated beforehand, and ‘in-train’, where perturbations are introduced dynamically during the training process, to understand the advantages and limitations of each approach in enhancing model robustness.
José María Jorquera Valero, Ibon Bengoechea Cazorla, Manuel Gil Pérez· IEEE Access· 0 citations
Adversarial training (AT) is a widely adopted defense against adversarial attacks, but its multi-step optimization process for generating adversarial examples leads to substantial computational overhead. To mitigate this, various single-step adversarial training methods have been proposed. However, these models often suffer from catastrophic overfitting under larger perturbations and exhibit degraded robustness. The core issue is that certain single-step adversarial examples, although successfully learned and correctly classified, fail to expose the true vulnerabilities of models. We refer to these misleading examples as “fakers”. Specifically, we find that fakers exhibit three distinct characteristics compared to standard adversarial examples: 1) they unexpectedly degrade the model robustness rather than improve it; 2) they make it harder for the model to learn their robust features; and 3) they show significantly greater divergence from their clean counterparts. These observations motivate us to proactively reduce the impact of fakers during training. To this end, we propose the Faker-Alleviating Single-step adversarial Training method (FAST), a general and effective framework designed to enhance both accuracy and robustness. Concretely, FAST consists of two main components. First, it dynamically adjusts the label-smoothing level for adversarial examples according to their learning difficulty, making fakers easier for the model to learn. Second, it introduces an auxiliary sample with a weak adversarial effect, derived from the single-step adversarial example, which is used to dynamically ease the alignment with clean data and stabilize the optimization process. We demonstrate the effectiveness of FAST through extensive experiments, showing that our method achieves superior clean accuracy and robustness against various types of adversarial attacks. The code is available at https://github.com/mesunhlf/FAST.
Lifeng Huang, Yuquan Lin, Chen Wan et al.· IEEE Transactions on Informa...· 0 citations
Adversarial attacks misled deep neural networks by injecting perturbations into input images. Training networks with adversarial examples defended against adversarial attacks. However, training with specific adversarial examples only defended against the corresponding attacks. To generalize the defense effect from one specific attack to other attacks, we proposed a method called Gradient Vicinity Adversarial Training (GVAT), which generated adversarial examples along directions sampled in the vicinity of the gradient. The defense effects of GVAT were evaluated using three attack methods: fast gradient sign method (FGSM), projected gradient descent (PGD), and Carlini–Wagner (CW) under the L2-norm constraint. A three-layer convolutional network was trained on the MNIST dataset, and two WideResNet-28-10 networks were trained on the CIFAR-10 and CIFAR-100 datasets respectively. Under the transfer-based black-box setting, the results showed that GVAT not only defended against the corresponding attacks that generated adversarial examples but also defended against other attacks. In other words, the defense effect of GVAT was generalized to other attacks under the transfer-based black-box setting.
The research methodology involved a systematic literature review using the Scopus database, adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, and focusing on recent advancements in attack and defence techniques.
This paper introduces DefendMal, a novel framework that synergistically combines Denoise Autoencoder with Sequence Squeezing, a Context-aware Adversarial Generator (CAG-AdvGAN), Projected Gradient Descent (PGD) adversarial training, and a Positive–Negative Detector with Variational Autoencoder (PNDetector-VAE) to enhance robustness against evolving adversarial threats.
Dennis Benedict Crasta, Vikash Kumar· Journal of Computer Virology...· 0 citations
A novel energy-based optimization strategy to improve the robust generalization of machine learning models against adversarial attacks by incorporating the principles of energy-based models and shows strong and competitive performance across three extensively utilized datasets.
