FPR manipulation attack perturbs benign MQTT packets to flip labels to attacks in NIDS with 80-100% success, increasing SOC delays without gradient-based methods.
Generating Adversarial Examples with Adversarial Networks
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high perceptual quality and more efficiently requires more research efforts. In this paper, we propose AdvGAN to generate adversarial examples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances. For AdvGAN, once the generator is trained, it can generate adversarial perturbations efficiently for any instance, so as to potentially accelerate adversarial training as defenses. We apply AdvGAN in both semi-whitebox and black-box attack settings. In semi-whitebox attacks, there is no need to access the original target model after the generator is trained, in contrast to traditional white-box attacks. In black-box attacks, we dynamically train a distilled model for the black-box model and optimize the generator accordingly. Adversarial examples generated by AdvGAN on different target models have high attack success rate under state-of-the-art defenses compared to other attacks. Our attack has placed the first with 92.76% accuracy on a public MNIST black-box attack challenge.
verdicts
UNVERDICTED 7representative citing papers
DarkLLM trains an LLM to generate language-driven adversarial perturbations that unify targeted, untargeted, segmentation, and multi-model attacks on foundation models.
RELO formulates visual object tracking localization as a Markov decision process solved by reinforcement learning with combined IoU and AUC rewards, augmented by layer-aligned temporal token propagation, and reports 57.5% AUC on LaSOText without template updates.
LiDAR-Adv generates adversarial objects to fool LiDAR-based autonomous driving detection systems, tested on Baidu Apollo and with physical 3D prints.
LocalAlign generates near-target adversarial examples via prompting and applies margin-aware alignment training to enforce tighter boundaries against prompt injection attacks.
TSPG applies conditional GANs to generate realistic transcriptome perturbations that mimic source-to-target gene expression state transitions and highlight biologically enriched genes.
AEGIS combines SemantiGAN filtering with evidential learning on five handcrafted instability metrics to detect adversarial attacks, reporting 92.1% AUROC on Tiny ImageNet across six attack types.
citing papers explorer
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Cellular State Transformations using Generative Adversarial Networks
TSPG applies conditional GANs to generate realistic transcriptome perturbations that mimic source-to-target gene expression state transitions and highlight biologically enriched genes.