Recent studies have highlighted adversarial examples as a ubiquitous threat to different neural network models and many downstream applications. Nonetheless, as unique data properties have inspired distinct and powerful learning principles, this paper aims to explore their potentials towards mitigating adversarial inpu…
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,982 papers · 148 categories
Trend · papers per month
48 results for “Adversarial Exploitation”
Mixup inference improves adversarial robustness by mixing inputs with clean samples.
problem Adversarial examples can fool deep networks due to local non-linearity.
method Develops mixup inference, which mixes inputs with clean samples to shrink adversarial perturbations.
result Mixup inference enhances adversarial robustness for mixup-trained models.
This paper shows excessive invariance in adversarial robust models can make them more vulnerable to certain types of attacks.
problem Excessive invariance in adversarial robust models can make them more vulnerable to certain types of attacks.
method Analytical constructions and empirical studies of vision classifiers with state-of-the-art robustness to perturbation-based adversaries constrained by an norm.
result Robustness to perturbation-based adversarial examples does not guarantee general robustness and can increase vulnerability to invariance-based adversarial examples.
The paper shows how imitation learning can be exploited to extract DRL policies.
problem The confidentiality and integrity of DRL policies are vulnerable to model extraction attacks.
method Developed proof-of-concept attacks using imitation learning techniques.
result Demonstrated the feasibility of launching black-box attacks against DRL policies.
Proposes a framework for extracting consistent physiological features across users.
problem Variability of biosignals across different users and tasks.
method Adversarial feature extractor for disentangled universal representations.
result Up to 8.8% improvement in average accuracy of classification.
Log-Loss scores expose membership privacy breaches.
problem Privacy leakage from statistical aggregates like Log-Loss scores.
method Proved that Log-Loss scores enable full accuracy membership inference in a single query.
result Complete membership privacy breach is possible with Log-Loss scores.
New method improves DNN robustness against adversarial attacks.
problem Adversarial vulnerability of neural networks.
method Enforcing linearity in DNN in transformed input and feature space, and augmenting with Local Lipschitz regularizer.
result Significantly improved robustness of DNN models on various datasets.
Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, currently there is no study of their robustness to adversarial attacks. Yet, in domains where they are likely to be used, e.g. the web, adversaries are common. Can deep learning models for …
Paper develops robust Bayesian models for linear regression under adversarial perturbations.
problem Ensuring reliable machine learning models under data perturbations.
method Formulates adversarial Bregman divergence loss, computes adversarial perturbation, introduces adversarially robust posteriors, derives generalization certificates.
result Derives first rigorous generalization certificates for adversarially robust Bayesian linear regression.
Neural networks are vulnerable to adversarial examples, malicious inputs crafted to fool trained models. Adversarial examples often exhibit black-box transfer, meaning that adversarial examples for one model can fool another model. However, adversarial examples may be overfit to exploit the particular architecture and …
Adversarial policies beat superhuman Go AI systems.
problem Vulnerability of superhuman AI systems to adversarial attacks.
method Training adversarial policies to trick KataGo into making blunders.
result Adversarial policies achieve >97% win rate against KataGo at superhuman settings.
Breaking certified defenses: Semantic adversarial examples with spoofed robustness certificatescs.LG
New attack tricks certifiably robust models into mislabeling images.
problem Defeating certified defenses against adversarial examples.
method Spoofed robustness certificates and large perturbations.
result Certifiably robust models can be fooled by large perturbations.
Automated discovery of adaptive attacks improves adversarial defense evaluation.
problem Challenges in reliably evaluating adversarial defenses.
method Formalizes adaptive attacks as reusable building blocks in a search space for automatic discovery.
result Our tool discovers significantly stronger attacks than AutoAttack, improving adversarial defense evaluation.
New method detects adversarial images by exploiting their density.
problem Detecting adversarial images that are hard to find with gradient methods.
method Develop a test based on the density of adversarial directions.
result Unprecedented accuracy in detecting adversarial attacks under white-box setting.
AdvFlow generates imperceptible adversarial images using normalizing flows.
problem Adversarial attacks on deep learning classifiers.
method Normalizing flows to model adversarial examples.
result Generated adversaries closely follow clean data distribution, making them harder to detect.
Improved LIME robustness against adversarial manipulation.
problem LIME's naive sampling strategy can be exploited to hide biased behavior.
method Training a GAN to generate more realistic synthetic data for explanations.
result Our method increases accuracy in detecting biased behavior compared to vanilla LIME.
Adversarial deep learning improves EEG-based person identification.
problem Exploiting temporally correlated structures and session variability in EEG data.
method Adversarial inference approach to learn session-invariant representations.
result Improvements in person identification robustness from longitudinal EEG data.
New adversarial attack method based on deep feature distributions.
problem Adversarial attacks on CNN classifiers using output layer information.
method Modeling and exploiting class-wise and layer-wise deep feature distributions.
result Achieves state-of-the-art transfer-based attack results for undefended ImageNet models.
Adversarial examples are malicious inputs designed to fool machine learning models. They often transfer from one model to another, allowing attackers to mount black box attacks without knowledge of the target model's parameters. Adversarial training is the process of explicitly training a model on adversarial examples,…
New research shows adversarial defenses increase risk of membership inference attacks.
problem Combining security and privacy domains to measure adversarial defenses' impact on membership inference attacks.
method Measuring the success of membership inference attacks against six state-of-the-art adversarial defense methods.
result Adversarial defense methods can increase the risk of membership inference attacks.
We investigate conditions under which test statistics exist that can reliably detect examples, which have been adversarially manipulated in a white-box attack. These statistics can be easily computed and calibrated by randomly corrupting inputs. They exploit certain anomalies that adversarial attacks introduce, in part…
Adversarial approach enhances sensor fusion for robust target detection.
problem Improving target detection and classification using multi-modal sensor fusion.
method Generative network learns latent space from various sensor modalities, then detects damaged sensors and safeguards performance.
result Automatic robustness against noisy/damaged sensors achieved.
Enhances adversarial example transferability by fine-tuning existing examples.
problem Adversarial examples are often overfit to a source model, limiting black-box transferability.
method Intermediate Level Attack (ILA) fine-tunes adversarial examples on a pre-specified layer of the source model.
result ILAs achieve high transferability to target models without knowledge of their architecture.
Proposes adversarial normalization for multi-domain image segmentation.
problem Current image normalization is per-dataset, limiting multi-domain segmentation.
method Adversarial training to learn common normalizing functions across multiple datasets.
result Optimal normalizer improves segmentation accuracy and realism.
This research identifies flaws in drift detection methods and creates adversarial data streams to exploit them.
problem The challenge of detecting data distribution changes (drift) in real-time systems.
method Developed adversarial data streams to show weaknesses in existing drift detection schemes.
result Demonstrated that common drift detection methods can be fooled by adversarial data streams.
Interval attacks find more adversarial examples than existing methods.
problem Evaluating robustness of adversarially trained neural networks against unknown attacks.
method Symbolic interval propagation for bound over-approximation and gradient-guided attacks.
result Interval attacks find on average 47% more violations than state-of-the-art methods.
New method improves robustness of smoothed classifiers against adversarial attacks.
problem Improving robustness of smoothed classifiers against adversarial attacks.
method Proposes worst-case adversarial loss over input distributions as a robustness certificate, and uses duality and smoothness properties to provide an easy-to-compute upper bound.
result Shows superior robustness performance over state-of-the-art certified or heuristic methods.
In this work we propose an adversarial learning approach to generate high resolution MRI scans from low resolution images. The architecture, based on the SRGAN model, adopts 3D convolutions to exploit volumetric information. For the discriminator, the adversarial loss uses least squares in order to stabilize the traini…
ScoreAG generates unrestricted adversarial images maintaining semantic integrity.
problem Limited robustness evaluations due to -norm constraints.
method Score-Based Adversarial Generation (ScoreAG) using score-based generative models.
result ScoreAG improves robustness assessments across multiple benchmarks.
The susceptibility of deep learning to adversarial attack can be understood in the framework of the Renormalisation Group (RG) and the vulnerability of a specific network may be diagnosed provided the weights in each layer are known. An adversary with access to the inputs and outputs could train a second network to clo…
LGV boosts adversarial attacks by improving surrogate models.
problem Improving the transferability of black-box adversarial attacks.
method LGV uses a pretrained surrogate model and multiple weight sets from additional training epochs to generate an effective surrogate ensemble.
result LGV outperforms other test-time transformations by significant margins.
New snooping attacks exploit deep RL without access to environment.
problem Security vulnerabilities in deep reinforcement learning.
method Proposes snooping threat models and attacks on RL agents.
result Adversaries can launch attacks without interacting with the environment.
New adversarial training methods generate multiplicative perturbations for robust DNN training.
problem Training Deep Neural Networks with adversarial examples to improve robustness.
method Proposes xAT and xVAT, generating multiplicative perturbations for robust training.
result xAT and xVAT match or outperform state-of-the-art classification accuracies and are faster.
Adversarial transfer learning improves stress assessment across users.
problem Transfer learning challenges in physiological biosignals.
method Disentangled nuisance-robust representations using adversarial networks.
result Adversarial framework enhances cross-subjects stress assessment.
Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Trainingcs.LG
New method reduces bias in NLI models using ensemble adversarial training.
problem Spurious correlations between hypotheses and entailment classes in NLI datasets.
method Adversarial training with an ensemble of classifiers to reduce bias in sentence representations.
result Ensemble adversarial training produces more robust NLI models, outperforming previous methods.
Robust RL improves controller robustness to dynamics variations using adversarial populations.
problem Robustness issues in RL when dynamics are perturbed.
method Adversarial population augmentation to the Robust RL formulation.
result Population-based adversarial approach yields more robust and generalizable policies.
Quantum mechanics is inherently probabilistic in light of Born's rule. Using quantum circuits as probabilistic generative models for classical data exploits their superior expressibility and efficient direct sampling ability. However, training of quantum circuits can be more challenging compared to classical neural net…
This work tackles learning Markov games with adversarial opponents and achieves both average reward and exploitation.
problem Achieving both average reward and exploiting adaptive opponents in Markov games.
method Develops efficient algorithms and proves hardness results for learning Markov games with adversarial opponents.
result Achieves -regret bounds for certain conditions on opponent policies, complemented by an exponential lower bound.
Recent research has demonstrated the brittleness of machine learning systems to adversarial perturbations. However, the studies have been mostly limited to perturbations on images and more generally, classification that does not deal with temporally varying inputs. In this paper we ask "Are adversarial perturbations po…
New method uses adversarial training for structural model estimation.
problem Estimating structural models with high accuracy.
method Adversarial training between generator and discriminator.
result Adversarial estimator achieves parametric efficiency under correct specification.
Paper shows adversarial training can be fooled by new type of noise.
problem Adversarial training can be fooled by new types of noise.
method Designing ADVIN, a new type of inducing noise.
result ADVIN can degrade adversarial training robustness by 99.9%.
Deep neural networks represent the state of the art in machine learning in a growing number of fields, including vision, speech and natural language processing. However, recent work raises important questions about the robustness of such architectures, by showing that it is possible to induce classification errors thro…
RNNs are vulnerable to adversarial attacks, especially in network traffic.
problem Adversarial attacks on RNNs for IDSs in network traffic.
method Developed new explainability techniques and ARS for comparing IDSs.
result RNNs are vulnerable to adversarial attacks, even in sequential data.
Study shows adversarial attacks can fool speech-to-text models, and PCA is ineffective as a defense.
problem Adversarial attacks can mislead speech-to-text neural networks.
method Crafted adversarial waveforms, used PCA for defense, tested under black-box setting.
result PCA is ineffective as a defense mechanism against adversarial attacks in audio domain.
New algorithm disentangles latent space in GANs using video sequences.
problem Learning disentangled latent spaces in GANs without supervision.
method Adversarial training with video sequences, modifying standard GAN algorithm.
result Disentangled latent space into content and motion attributes.
Optimizes deep learning training by treating it as an optimal control problem.
problem Fragility of deep neural networks to adversarial inputs.
method Formulates adversarial training as a min-max optimization problem and interprets it as an optimal control problem.
result Provides the first convergence analysis of adversarial training algorithm.
Anomaly detection method tackles hidden adversary actions in real-world scenarios.
problem Detecting hidden adversary actions in real-world scenarios where the defender cannot perfectly observe the attacker's actions.
method Extends existing anomaly detection models to handle continuous action spaces and game-theoretic framework. Proposes two algorithms: direct extension and learning-based approach.
result Learning-based approach produces less exploitable strategies and is scalable to higher dimensions.
Domain knowledge helps detect adversarial examples in multi-label classification.
problem Detecting adversarial examples in multi-label classification.
method Convert domain knowledge into constraints and inject them into a semi-supervised learning problem.
result Domain-knowledge constraints help detect adversarial examples effectively.