Despite the remarkable performance of deep neural networks on various computer vision tasks, they are known to be susceptible to adversarial perturbations, which makes it challenging to deploy them in real-world safety-critical applications. In this paper, we conjecture that the leading cause of adversarial vulnerabili…
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48 results for “vulnerable features”
Increasing numbers of software vulnerabilities are discovered every year whether they are reported publicly or discovered internally in proprietary code. These vulnerabilities can pose serious risk of exploit and result in system compromise, information leaks, or denial of service. We leveraged the wealth of C and C++ …
A model disentangles features to detect adversarial inputs.
problem Detecting and defending against adversarial attacks on neural networks.
method Proposes a minimax game formulation using variational autoencoders to separate robust and vulnerable features.
result Adversarial inputs cannot bypass the detector without semantic change, indicating successful detection.
The detection of software vulnerabilities (or vulnerabilities for short) is an important problem that has yet to be tackled, as manifested by the many vulnerabilities reported on a daily basis. This calls for machine learning methods for vulnerability detection. Deep learning is attractive for this purpose because it a…
HarDNN detects and protects CNNs from hardware errors.
problem Hardware errors in CNNs can corrupt inference output.
method Statistical error injection and heuristic vulnerability assessment.
result HarDNN improves CNN resilience with minimal additional computation.
Each year, thousands of software vulnerabilities are discovered and reported to the public. Unpatched known vulnerabilities are a significant security risk. It is imperative that software vendors quickly provide patches once vulnerabilities are known and users quickly install those patches as soon as they are available…
Pricing vulnerable options in a hybrid credit risk model driven by Heston-Nandi GARCH processesq-fin.PR
Hybrid model prices vulnerable options with stochastic volatility.
problem Pricing options with stochastic volatility and credit risk.
method Closed-form hybrid credit risk model with Heston-Nandi GARCH processes.
result Explicit pricing formula for vulnerable options.
DVERGE diversifies adversarial vulnerabilities to enhance robust ensemble models.
problem Diverse adversarial vulnerabilities for robust ensemble models.
method Isolates and diversifies adversarial vulnerabilities through distillation and training.
result Achieves higher robustness against transfer attacks compared to previous methods.
Paper shows large models are vulnerable to poisoning attacks.
problem Vulnerability of large machine learning models to poisoning attacks.
method Analyzed isotropic random feature vectors and geometric median as robust gradient aggregator rule.
result Linear and logistic regressions with parameters are vulnerable to arbitrary manipulation by poisoners.
Thousands of security vulnerabilities are discovered in production software each year, either reported publicly to the Common Vulnerabilities and Exposures database or discovered internally in proprietary code. Vulnerabilities often manifest themselves in subtle ways that are not obvious to code reviewers or the develo…
Paper tackles model vulnerabilities by reconstructing training data.
problem Reconstructing training data from model parameters poses a security risk.
method Developed a mathematical framework and score matching method for both Bayesian and non-Bayesian models.
result First score matching framework for reconstructing data in Bayesian models.
Devign uses graph neural networks to identify vulnerabilities efficiently.
problem Challenging and tedious process of identifying vulnerabilities in software systems.
method Devign employs a graph neural network to classify graph-level vulnerabilities using comprehensive code semantic representations.
result Devign significantly outperforms state-of-the-art models in vulnerability identification.
AFD learns features resistant to adversarial attacks.
problem Vulnerability of neural networks to adversarial attacks.
method AFD learns features invariant to adversarial perturbations through a game of predictive and robust features.
result AFD effectively resists a wide range of attack types and strengths.
Defense against adversarial attacks by manipulating feature thickness.
problem Vulnerability of machine learning models to adversarial attacks.
method Feature Manipulation (FM)-Defense using a combo-variational autoencoder.
result Detection and purification of adversarial examples with high accuracy.
New insights into adversarial vulnerability linked to manifold separability issues.
problem Adversarial vulnerability in machine learning models.
method Characterized data distribution as a low-dimensional manifold, focusing on on/off manifold directions and the impact of first-order vs. second-order optimization methods.
result First-order optimization methods lead to poor convergence in the off-manifold direction, causing adversarial vulnerability in inseparable datasets.
Paper improves DNS typo-squatting detection with ensemble model.
problem Detecting and preventing DNS typo-squatting attacks.
method Ensemble-based feature selection and bagging classification model.
result The proposed framework achieves high accuracy and precision in identifying typo-squatting domains.
One of the main drawbacks of deep neural networks, like many other classifiers, is their vulnerability to adversarial attacks. An important reason for their vulnerability is assigning high confidence to regions with few or even no feature points. By feature points, we mean a nonlinear transformation of the input space …
Optimal adversarial attacks minimize mutual information, revealing classifier vulnerabilities.
problem Designing optimal attacks to degrade machine learning performance.
method Information-theoretic approach to finding optimal perturbations.
result Optimal attacks minimize mutual information between degraded and original signals.
This research examines how data transformations affect adversarial robustness in recurrent neural networks.
problem Adversarial examples reduce machine learning accuracy, especially in high-dimensional datasets.
method Analysis of feature selection, dimensionality reduction, and trend extraction techniques on recurrent neural networks.
result Data transformations may increase vulnerability to adversarial samples, but only if they approximate intrinsic dimensionality and maintain manifold coverage.
In this paper, we propose a new deep feature selection method based on deep architecture. Our method uses stacked auto-encoders for feature representation in higher-level abstraction. We developed and applied a novel feature learning approach to a specific precision medicine problem, which focuses on assessing and prio…
Feature Averaging: An Implicit Bias of Gradient Descent Leading to Non-Robustness in Neural Networkscs.LG
Gradient descent biases neural networks to use an average of features, leading to non-robustness.
problem Non-robustness in neural networks due to feature averaging.
method Theoretical analysis and experiments on binary classification tasks.
result Gradient descent trains networks to rely on an average of features, making them vulnerable to adversarial attacks.
Neural networks are vulnerable to adversarial attacks -- small visually imperceptible crafted noise which when added to the input drastically changes the output. The most effective method of defending against these adversarial attacks is to use the methodology of adversarial training. We analyze the adversarially train…
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.
Despite their impressive performance, deep neural networks exhibit striking failures on out-of-distribution inputs. One core idea of adversarial example research is to reveal neural network errors under such distribution shifts. We decompose these errors into two complementary sources: sensitivity and invariance. We sh…
Deep neural networks were shown to be vulnerable to single pixel modifications. However, the reason behind such phenomena has never been elucidated. Here, we propose Propagation Maps which show the influence of the perturbation in each layer of the network. Propagation Maps reveal that even in extremely deep networks s…
Graph Backdoorcs.LG
GTA is the first backdoor attack on GNNs, demonstrating vulnerabilities in graph-oriented security models.
problem Vulnerability of graph neural networks to backdoor attacks.
method Graph-oriented triggers, dynamic adaptation, model-agnostic, attack-extensible.
result Demonstrates severe threats to graph classification and node classification tasks.
Paper proposes a new ML approach to estimate g-vulnerability without estimating conditional probabilities.
problem Estimating information leakage in black-box systems with large output domains.
method Developed a novel ML-based approach to estimate g-vulnerability without estimating conditional probabilities.
result The proposed method outperforms frequentist approach when dealing with large output domains.
AAT separates robust and non-robust features without supervision.
problem Adversarial vulnerability and accuracy reduction in machine learning models.
method Adversarial Asymmetric Training (AAT) algorithm.
result Preserves accuracy and achieves better disentanglement than previous methods.
Adapts IG for better feature attributions and robustness.
problem Reliability concerns in feature attributions for deep learning models.
method Adaptation of path-based feature attribution to Riemannian geometry of data manifolds.
result IG along geodesics generates more intuitive and robust explanations.
This paper analyzes how data and model properties affect membership inference attacks.
problem Understanding and mitigating the vulnerability of machine learning models to membership inference attacks.
method Empirical analysis of data and model properties on MIA success.
result Data and model properties, not just model overfitting, influence MIA success.
CausAdv detects adversarial examples using causal reasoning.
problem Vulnerability of CNNs to adversarial perturbations.
method Causal framework based on counterfactual reasoning.
result Adversarial examples exhibit different CI distributions compared to clean samples.
A membership inference attack (MIA) against a machine-learning model enables an attacker to determine whether a given data record was part of the model's training data or not. In this paper, we provide an in-depth study of the phenomenon of disparate vulnerability against MIAs: unequal success rate of MIAs against diff…
New holistic approach measures sample-level adversarial vulnerability for trustworthy systems.
problem Inherent bias in adversarial attacks across subgroups.
method Combining high-frequency feature reliance and sample-distance to decision boundary.
result Holistic approach improves adversarial vulnerability estimation and system trustworthiness.
Study robustness of global feature effect explanations in machine learning models.
problem Vulnerability of global feature effect explanations to data and model perturbations.
method Theoretical bounds and experimental evaluation of partial dependence plots and accumulated local effects.
result Quantifies the gap between best and worst-case scenarios of misinterpreting machine learning predictions globally.
Overparametrized models are vulnerable to adversarial perturbations, affecting robust generalization.
problem Understanding how overparametrization impacts robustness in adversarial training.
method Analyzing random features regression models with a precise asymptotic formula.
result High overparametrization can hurt robust generalization in adversarially trained models.
Study on adversarial training's impact on deep neural reinforcement learning policies.
problem Vulnerability of deep neural reinforcement learning policies to imperceptible adversarial perturbations.
method Two parallel approaches: Fourier spectrum analysis and feature sensitivity measurement.
result Adversarially trained policies are more sensitive to low frequency perturbations.
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks, where small perturbations intentionally added to the original inputs can fool the classifier. In this paper, we propose a defense method, Featurized Bidirectional Generative Adversarial Networks (FBGAN), to extract the semantic feature…
Confounding biases recommender systems, even when data seems fully observed.
problem Unmeasured features influencing both treatment and outcome in recommender systems.
method Illustrations and simulation studies showing how common practices introduce confounding.
result Standard recommender system practices can introduce confounding, reducing performance.
Model for optimal cybersecurity investment considering clustered cyberattacks.
problem Optimal investment in cybersecurity to reduce system vulnerability under clustered cyberattacks.
method Developed a continuous-time stochastic model using a Hawkes process, extended Gordon-Loeb model, solved as a Markovian stochastic optimal control problem.
result Investment policies that account for attack clustering lead to more effective and responsive strategies, improving upon static and Poisson-based approaches.
Over the past few years, neural networks were proven vulnerable to adversarial images: targeted but imperceptible image perturbations lead to drastically different predictions. We show that adversarial vulnerability increases with the gradients of the training objective when viewed as a function of the inputs. Surprisi…
Massif helps interpret adversarial attacks on deep learning models.
problem Vulnerability of deep learning models to adversarial attacks.
method Interactive tool for visualizing and interpreting neurons affected by adversarial attacks.
result Massif provides a high-level overview and detailed low-level analysis of adversarial effects.
BN helps learn fragile features, which can improve adversarial robustness.
problem The role of batch normalization in adversarial training and its impact on robustness.
method Investigated the expressiveness of BN in learning robust features compared to random features.
result Adversarially fine-tuning BN layers can result in non-trivial adversarial robustness.
I-GCN improves GCNs' robustness against adversarial attacks.
problem Adversarial attacks degrade GCNs' performance in security-critical applications.
method Influence mechanism divides node effects into introverted and extroverted influences.
result I-GCN achieves higher accuracy rates than state-of-the-art methods in defending against adversarial attacks.
Deep learning has been successfully used in numerous applications because of its outstanding performance and the ability to avoid manual feature engineering. One such application is electroencephalogram (EEG) based brain-computer interface (BCI), where multiple convolutional neural network (CNN) models have been propos…
On Vulnerability Conditional Risk Measures: Comparisons and Applications in Cryptocurrency Marketq-fin.RM
New risk measures assess cryptocurrency market vulnerabilities during financial distress.
problem Capturing systemic risk in cryptocurrency markets during financial distress.
method Introducing Vulnerability Conditional Risk Measures (VCoES) and related measures.
result Validated theoretical insights and demonstrated practical relevance in cryptocurrency market.
Comparing the collective behavior of banking industryphysics.soc-ph
The study examines collective behavior in banking sectors across mature and emerging markets.
problem Understanding collective behavior in banking sectors across different market types.
method Applied Random Matrix Theory (RMT) to analyze the banking sectors of 4 world stock markets.
result Mature markets exhibit higher collective behavior compared to emerging markets.
Preschool attendance correlates with lower developmental vulnerabilities in Queensland, Australia.
problem Understanding the relationship between preschool attendance and developmental vulnerabilities in different regions.
method Data Analysis and Machine Learning to identify clusters of socio-demographic variables.
result Identified three clusters with varying socio-demographic variables affecting the relationship between preschool attendance and developmental vulnerabilities.
Deep Learning detects cyclists' orientation for safer roads.
problem Ensuring safety for cyclists in intelligent transportation systems.
method Transfer Learning with pre-trained models, multi-class detection, new dataset creation.
result Faster R-CNN with ResNet50 proved precise but slower; SSD with InceptionV2 provided good trade-off.