Pixel discretization fails against advanced adversarial attacks.
problem Understanding limitations of pixel discretization against adversarial attacks.
method Study of pixel discretization defense methods, including variants.
result Pixel discretization is ineffective against strong adversarial attacks.
New defense method for non-parametric classifiers robust against adversarial attacks.
problem Lack of robustness in non-parametric classifiers against adversarial attacks.
method Adversarial pruning method to preprocess datasets and a novel attack.
result Adversarial pruning provides a robust defense for non-parametric classifiers.
ME-Net defends neural nets against adversarial attacks by reconstructing images.
problem Adversarial attacks on deep neural networks.
method ME-Net uses matrix estimation to preprocess images, destroying adversarial noise while preserving global structure.
result ME-Net consistently outperforms state-of-the-art defenses on various benchmarks.
TensorShield defends images from adversarial attacks using tensor decomposition.
problem Adversarial attacks on images can fool deep neural networks.
method Tensor decomposition to find low-rank approximations of images, reducing high-frequency perturbations.
result TensorShield outperforms existing methods like SLQ by 14% against FGSM attacks.
Study shows dataset properties impact adversarial machine learning robustness.
problem Vulnerability of DNNs to adversarial attacks.
method Examined five datasets, analyzed input size and contrast effects.
result Input size and contrast significantly influence adversarial success.
3D point cloud attacks examine how neural networks can be fooled.
problem Understanding how 3D neural networks can be exploited by attackers.
method Examined two categories of attacks: distributional and shape attacks.
result Some shape attacks can fool 3D point cloud classification models even after preprocessing.
DPASF stream preprocesses Big Data streams efficiently.
problem Efficient preprocessing of streaming Big Data.
method Implemented six preprocessing algorithms in Apache Flink.
result Preprocessing improves data accuracy in streaming Big Data.
ATHENA builds flexible defenses against adversarial attacks.
problem Extensive research on adversarial attacks is domain-specific and cannot be easily extended.
method Designing an extensible framework based on diverse weak defenses.
result Comprehensive empirical study demonstrates the effectiveness of ATHENA.
Data preprocessing improves data quality for robust data mining.
problem Noisy and incomplete data hinders data mining models.
method Overview of data cleaning, transformation, and preprocessing methods.
result Preprocessing significantly affects data mining model performance.
Automated trading system with preprocessing and reinforcement learning.
problem Efficient portfolio trading for individual investors.
method Feature preprocessing, recurrent reinforcement learning, automated trading algorithm.
result System outperforms other strategies in profit and drawdown.
New defense method inspired by encryption improves visual classification accuracy.
problem Conventional defenses reduce accuracy and are defeated by obfuscated gradients.
method Block-wise pixel shuffling with secret key for training and test images.
result Achieves high accuracy (91.55%) on clean images and (89.66%) on adversarial examples.
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 research shows many recent defenses against adversarial examples are ineffective against black-box attacks.
problem The robustness of recent defenses against adversarial examples is insufficient, especially against black-box attacks.
method Evaluation of nine defenses on two black-box adversarial models and six attacks on CIFAR-10 and Fashion-MNIST datasets.
result Most recent defenses provide only marginal improvements in security (<25%) compared to undefended networks. A framework uses preprocessing to improve psychiatric questionnaire predictions while maintaining interpretability.
problem Weak predictive accuracy and limited interpretability of psychiatric questionnaires.
method Two-stage method: stable preprocessing followed by a linear mapping.
result REFINE outperforms other interpretable approaches in psychiatric and non-psychiatric prediction tasks.
Paper discusses methods to evaluate defenses against adversarial examples.
problem Difficulty in evaluating adversarial robustness.
method Methodological foundations and best practices for evaluating defenses.
result Suggests new methods to avoid common pitfalls in evaluations.
Metalearning finds preprocessing often hinders accuracy, but can improve pipelines.
problem The role of preprocessing in machine learning pipelines is underexplored.
method Extensive empirical study over various algorithms and preprocessors using metalearning.
result Metalearning suggests preprocessing should be used in ML pipeline design.
This paper tackles dynamic ensemble selection and data preprocessing for multi-class imbalance learning.
problem Class imbalance in multi-class datasets where majority class has more instances.
method Examined five preprocessing methods and four dynamic selection methods for multi-class imbalanced problems.
result Dynamic ensemble improves F-measure and G-mean compared to static ensemble.
Enhances solar flare prediction with advanced preprocessing and contrastive learning.
problem Accurate prediction of solar flares to mitigate risks to astronauts and equipment.
method Advanced data preprocessing pipeline and contrastive learning with GRU regression model.
result Exceptional True Skill Statistic (TSS) scores, surpassing previous methods.
Study defenses against data poisoning attacks in online learning.
problem Data poisoning attacks on machine learning models.
method Rigorous study of four standard defenses in both a powerful and a realistic threat model.
result The effectiveness of defenses depends on the ease of the learning problem.
This paper analyzes dynamic ensemble selection and preprocessing for multi-class imbalanced datasets.
problem Class imbalance in multi-class datasets where majority classes have more instances.
method Examined dynamic selection techniques and data preprocessing methods for multi-class imbalanced problems.
result Dynamic ensemble improves AUC and G-mean compared to static ensemble.
Paper analyzes adversarial attacks and defenses using game theory.
problem Unclear conditions for optimal attacks and defenses in adversarial learning.
method Game-theoretic framework with locally linear decision boundary model.
result Fast Gradient Method attack and Randomized Smoothing defense form a Nash Equilibrium.
Topology-aware generative models improve manifold-based defenses against adversarial examples.
problem Adversarial examples compromise the reliability of ML models, especially DNNs.
method Investigate if generative models used in manifold-based defenses need to be topology-aware.
result Topology-aware generative models enhance the robustness of manifold-based defenses.
New defense against DNN model stealing attacks by poisoning predictions.
problem Stealing DNN models through black-box access attacks.
method Active perturbation of predictions to mislead attacker's training.
result Effective against a wide range of DNN stealing attacks, reducing attacker's success rate significantly.
Enhances diffusion models by preprocessing data to improve reconstruction quality.
problem Slow sampling and poor reconstruction quality in diffusion models, especially for small-scale networks.
method Applying Gaussianization preprocessing to the training data to make the target distribution more Gaussian-like.
result Improves generation quality, especially in the early stages of reconstruction with small networks.
Paper presents certified defenses against adversarial patch attacks.
problem Certified defenses against adversarial patch attacks are needed.
method Proposes the first certified defense and faster training methods.
result Demonstrates robustness transfer across different patch shapes.
First certified defense scaling to large datasets and models.
problem Robustness against adversarial examples in machine learning models.
method PixelDP, based on differential privacy.
result First certified defense that scales to large networks and models.
This paper uses feature preprocessing and RRL to automate profitable financial trading.
problem Automating profitable financial trading strategies.
method Feature preprocessing (PCA, DWT) followed by Recurrent Reinforcement Learning (RRL).
result The proposed strategy is effective, robust, and mitigates RRL's drawbacks.
This study compares different data preprocessing methods for gait analysis.
problem Improving the generalizability of machine learning models in gait analysis.
method Compared various data preprocessing steps including filtering, time derivative, normalization, and scaling.
result Different preprocessing combinations affect gait classification performance.
New adaptive attacks bypass many defenses to adversarial examples.
problem Adversarial example defenses are not adequately evaluated using adaptive attacks.
method Detailed analysis of thirteen defenses, demonstrating their vulnerabilities to adaptive attacks.
result Adversarial example defenses are more vulnerable to adaptive attacks than previously thought.
Proposes a new feature preprocessing method using kernel density integral transformation.
problem Feature preprocessing for tabular data in machine learning and statistics.
method Kernel density integral transformation as a drop-in replacement or improved alternative to min-max scaling and quantile transformation.
result Frequently outperforms min-max scaling and quantile transformation with hyperparameter tuning.
This study evaluates adversarial attacks and defenses for chest X-ray disease classification.
problem Vulnerability of deep neural networks to adversarial examples in chest X-ray disease detection.
method Detailed introduction and evaluation of various attack and defense methods.
result Attack and defense methods perform poorly with excessive iterations and large perturbations.
Stochastic defense improves natural classifiers against adversarial attacks.
problem Vulnerability of deep networks to adversarial attacks.
method Long-run MCMC sampling with Energy-Based Model for adversarial purification.
result Balancing memoryless and metastable behavior leads to effective purification and robust classification.
Paper presents a defense framework against adversarial examples.
problem Vulnerability of deep neural networks to adversarial examples.
method Cross-layer strategic ensemble defense with input and output transformations.
result Strategic ensemble defense achieves high defense success rates and robustness.
Defense against small image patches using occlusions.
problem Vulnerability of deep learning to small adversarial patches.
method Partially occlude image around each patch location.
result Certified security against patch attacks of a certain size.
This paper introduces defensive dropout to protect deep neural networks from adversarial attacks.
problem Vulnerability of deep neural networks to adversarial attacks.
method Proposes using dropout at test time to enhance robustness, modeling the problem as a game between attacker and defender.
result Defensive dropout reduces adversarial attack success rate significantly, from 100% to 13.89% on MNIST.
Paper introduces a new method to create adversarial examples against gradient-obfuscating defenses.
problem Crafting adversarial examples to fool gradient-obfuscating defenses.
method Stochastic Substitute Training (SST), a gray-box approach.
result Adversaries can create adversarial examples without knowledge or limited information about the defense.
Two preprocessing techniques reduce neural network training cost.
problem Training over-parameterized neural networks efficiently.
method Two novel preprocessing techniques to reduce training cost.
result Training cost reduced to sublinear per iteration.
Researchers improve defensive distillation to better protect machine learning models from adversarial attacks.
problem Machine learning models are vulnerable to adversarial examples that can cause misclassification.
method Revisit and improve defensive distillation as a defense mechanism.
result The improved defensive distillation is an effective way to address recent adversarial attacks.
Defense against ASR attacks using dropout uncertainty.
problem Adversarial attacks on ASR systems.
method Dropout uncertainty in neural networks.
result High detection accuracy across various ASR systems and datasets.
Survey of algorithms to correct past mistakes in prediction.
problem Improving prediction accuracy by correcting past errors.
method Defensive Forecasting as a sequential game theory approach to minimize prediction metrics.
result Simple, near-optimal algorithms for various prediction tasks.
This paper evaluates defenses against adversarial attacks on neural networks.
problem Adversarial attacks can trick DNNs into mis-classifying inputs.
method Scientific evaluation methodology for randomized defenses.
result RPENNs outperformed other defenses against adversarial attacks.
CVPR 2018 defenses ineffective against adversarial examples.
problem Vulnerability of neural networks to adversarial examples.
method Evaluated two white-box defenses from CVPR 2018.
result Defenses reduced model accuracy to 0%.
Unsupervised preprocessing can bias cross-validation estimates in regression models.
problem Bias in cross-validation estimates due to unsupervised preprocessing.
method Analysis of three preprocessing procedures: feature selection, grouping, and rescaling.
result Unsupervised preprocessing can introduce substantial bias into cross-validation estimates.
This paper investigates the impact of unconventional preprocessors on deep convolutional neural networks for face identification.
problem The impact of unconventional preprocessors on deep convolutional neural networks for face identification.
method Varying the preprocessing module across different approaches while keeping other network architecture facets constant, using transfer learning from Inception-V3 network features.
result Improvement in discriminative capability of deep networks through preprocessing with HE, quantization, and other methods.
This paper benchmarks time-series adversarial defenses and attacks.
problem Adversarial attacks on time-series data.
method Detailed benchmarking of adversarial defense methods in the L∞ threat model. result Adversarial defenses offer robustness against both strong white-box and black-box attacks.
Defense-GAN defends deep neural networks from adversarial attacks using generative models.
problem Vulnerability of deep neural networks to adversarial perturbations.
method Defense-GAN models the distribution of unperturbed images and finds a close output to a given image without adversarial changes.
result Defense-GAN consistently improves over existing defense strategies and works against various attack methods.
New method speeds up neural network training by preprocessing weight-data correlation.
problem Slow neural network training due to high time complexity.
method Stores weight-data correlation in a tree structure for quick detection of firing neurons.
result Achieves o(nmd) time per iteration with only O(nmd) preprocessing time. The paper reviews defenses against adversarial examples in deep learning.
problem Adversarial examples in deep learning models.
method Summarizes existing defenses and future research directions.
result Recommendations for enhancing security in deep learning models.