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Enhancing robustness of machine learning systems via data transformations
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-642-40994-3_25openalex-snapshot
Evasion Attacks against Machine Learning at Test Time
inferredcites →https://doi.org/10.1016/j.neucom.2018.04.027openalex-snapshot
Understanding adversarial training: Increasing local stability of supervised models through robust optimization
inferredcites →https://doi.org/10.1016/j.neunet.2012.02.023openalex-snapshot
Multi-column deep neural network for traffic sign classification
inferredcites →https://doi.org/10.1017/s0305004100030929openalex-snapshot
On best approximate solutions of linear matrix equations
inferredcites →https://doi.org/10.1109/cvpr.2014.220openalex-snapshot
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
inferredcites →https://doi.org/10.1109/cvpr.2015.7298640openalex-snapshot
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
inferredcites →https://doi.org/10.1109/cvprw.2017.171openalex-snapshot
Deceiving Google’s Cloud Video Intelligence API Built for Summarizing Videos
inferredcites →https://doi.org/10.1109/eurosp.2016.36openalex-snapshot
The Limitations of Deep Learning in Adversarial Settings
inferredcites →https://doi.org/10.1109/icassp.2013.6638293openalex-snapshot
Large-scale malware classification using random projections and neural networks
inferredcites →https://doi.org/10.1109/sp.2016.41openalex-snapshot
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot
Towards Evaluating the Robustness of Neural Networks
inferredcites →https://doi.org/10.1109/tcyb.2015.2415032openalex-snapshot
Adversarial Feature Selection Against Evasion Attacks
inferredcites →https://doi.org/10.1145/1128817.1128824openalex-snapshot
Can machine learning be secure?
inferredcites →https://doi.org/10.1145/1639562.1639592openalex-snapshot
Stealthy poisoning attacks on PCA-based anomaly detectors
inferredcites →https://doi.org/10.1145/1654988.1654990openalex-snapshot
A framework for quantitative security analysis of machine learning
inferredcites →https://doi.org/10.1145/2996758.2996771openalex-snapshot
Secure Kernel Machines against Evasion Attacks
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1145/3097983.3098158openalex-snapshot
Adversary Resistant Deep Neural Networks with an Application to Malware Detection
inferredcites →https://doi.org/10.1145/502512.502546openalex-snapshot
Random projection in dimensionality reduction
inferredcites →https://doi.org/10.1186/s13635-016-0045-0openalex-snapshot
Hidost: a static machine-learning-based detector of malicious files
inferredcites →https://doi.org/10.1201/9781351251389-8openalex-snapshot
Adversarial Examples in the Physical World
inferredcites →https://doi.org/10.14722/ndss.2014.23247openalex-snapshot
Drebin: Effective and Explainable Detection of Android Malware in Your Pocket
inferredcites →https://doi.org/10.14722/ndss.2016.23078openalex-snapshot
When a Tree Falls: Using Diversity in Ensemble Classifiers to Identify Evasion in Malware Detectors
inferredcites →https://doi.org/10.1561/9781601981479openalex-snapshot
Email Spam Filtering: A Systematic Review
inferredcites →https://doi.org/10.4230/lipics.ecoop.2020.15openalex-snapshot
Static Analysis of Shape in TensorFlow Programs
inferredcites →https://doi.org/10.48550/arxiv.1103.0398openalex-snapshot
Natural Language Processing (almost) from Scratch
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.48550/arxiv.1206.6389openalex-snapshot
Poisoning Attacks against Support Vector Machines
inferredcites →https://doi.org/10.48550/arxiv.1404.1100openalex-snapshot
A Tutorial on Principal Component Analysis
inferredcites →https://doi.org/10.48550/arxiv.1412.5068openalex-snapshot
Towards Deep Neural Network Architectures Robust to Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1509.07892openalex-snapshot
Evasion and Hardening of Tree Ensemble Classifiers
inferredcites →https://doi.org/10.48550/arxiv.1511.03034openalex-snapshot
Learning with a Strong Adversary
inferredcites →https://doi.org/10.48550/arxiv.1511.04599openalex-snapshot
DeepFool: a simple and accurate method to fool deep neural networks
inferredcites →https://doi.org/10.48550/arxiv.1511.06292openalex-snapshot
Foveation-based Mechanisms Alleviate Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1603.05145openalex-snapshot
Suppressing the Unusual: towards Robust CNNs using Symmetric Activation Functions
inferredcites →https://doi.org/10.48550/arxiv.1605.07277openalex-snapshot
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
inferredcites →https://doi.org/10.48550/arxiv.1608.00530openalex-snapshot
Early Methods for Detecting Adversarial Images
inferredcites →https://doi.org/10.48550/arxiv.1608.00853openalex-snapshot
A study of the effect of JPG compression on adversarial images
inferredcites →https://doi.org/10.48550/arxiv.1608.02128openalex-snapshot
Spoofing 2D Face Detection: Machines See People Who Aren't There
inferredcites →https://doi.org/10.48550/arxiv.1608.07690openalex-snapshot
A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1608.08967openalex-snapshot
Robustness of classifiers: from adversarial to random noise
inferredcites →https://doi.org/10.48550/arxiv.1705.02900openalex-snapshot
Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression
inferredcites →https://doi.org/10.48550/arxiv.1705.07204openalex-snapshot
Ensemble Adversarial Training: Attacks and Defenses
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Effectiveness of Adversarial Benign and Malware Examples in Evasion and Poisoning Attacks
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Federated Learning: An Overview of Attacks and Defense Methods
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Emphasizing Similar Feature Representations to Defend Against Adversarial Attacks
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Adversarial Deep Learning Attacks—A Review
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How to Defend and Secure Deep Learning Models Against Adversarial Attacks in Computer Vision: A Systematic Review
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On defending against label flipping attacks on malware detection systems
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Prediction of Covid-19 Disease Based on Underlying Medical Conditions: An Indian Scenario
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An Analytical Framework for Evaluating Successful Poisoning Attacks on Machine Learning Algorithms
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FineFool: A novel DNN object contour attack on image recognition based on the attention perturbation adversarial technique
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A taxonomy and survey of attacks against machine learning
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Robust dimensionality reduction for data visualization with deep neural networks
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Machine Learning-assisted immunophenotyping of peripheral blood identifies innate immune cells as best predictor of response to induction chemo-immunotherapy in head and neck squamous cell carcinoma – knowledge obtained from the CheckRad-CD8 trial
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Topological safeguard for evasion attack interpreting the neural networks’ behavior
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Detecting adversarial examples by additional evidence from noise domain
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Detection of Adversarial Examples Through Chaotic Features Extracted From Ordinal Patterns
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Data-Driven Development of Three-Dimensional Subsurface Models from Sparse Measurements Using Bayesian Compressive Sampling: A Benchmarking Study
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Data-driven photometric redshift estimation from type Ia supernovae light curves
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A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View
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Dimensionality Reduction, Regularization, and Generalization in Overparameterized Regressions
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Moving target defense for embedded deep visual sensing against adversarial examples
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Lateralized learning for robustness against adversarial attacks in a visual classification system
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Countering Acoustic Adversarial Attacks in Microphone-equipped Smart Home Devices
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Adversarial Examples on Object Recognition
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Research Progress and Challenges on Application-Driven Adversarial Examples: A Survey
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Data Synthesis for Testing Black-Box Machine Learning Models
inferred← citeshttps://doi.org/10.1145/3564625.3564661openalex-snapshot
Make Data Reliable: An Explanation-powered Cleaning on Malware Dataset Against Backdoor Poisoning Attacks
inferred← citeshttps://doi.org/10.1145/3571600.3571615openalex-snapshot
A Globally-Connected and Trainable Hierarchical Fine-Attention Generative Adversarial Network based Adversarial Defense
inferred← citeshttps://doi.org/10.1145/3615336openalex-snapshot
Generative Adversarial Networks: A Survey on Attack and Defense Perspective
inferred← citeshttps://doi.org/10.1145/3627536openalex-snapshot
The Path to Defence: A Roadmap to Characterising Data Poisoning Attacks on Victim Models
inferred← citeshttps://doi.org/10.1145/3636534.3690684openalex-snapshot
LightPure: Realtime Adversarial Image Purification for Mobile Devices Using Diffusion Models
inferred← citeshttps://doi.org/10.1145/3641861openalex-snapshot
Adversarial Transferability in Embedded Sensor Systems: An Activity Recognition Perspective
inferred← citeshttps://doi.org/10.1145/3665926openalex-snapshot
A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities
inferred← citeshttps://doi.org/10.1145/3691620.3695008openalex-snapshot
MaskDroid: Robust Android Malware Detection with Masked Graph Representations
inferred← citeshttps://doi.org/10.1145/3708497openalex-snapshot
Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach
inferred← citeshttps://doi.org/10.1155/2021/4280328openalex-snapshot
A Novel Defensive Strategy for Facial Manipulation Detection Combining Bilateral Filtering and Joint Adversarial Training
inferred← citeshttps://doi.org/10.1155/2022/2792639openalex-snapshot
Blockchain‐Enabled Smart Surveillance System with Artificial Intelligence
inferred← citeshttps://doi.org/10.1155/2022/6124895openalex-snapshot
[Retracted] Adversarial Attacks Defense Method Based on Multiple Filtering and Image Rotation
inferred← citeshttps://doi.org/10.11606/t.11.2023.tde-03112023-103445openalex-snapshot
A synergistic approach to sugarcane yield forecasting using machine learning, remote sensing, and process-based modeling
inferred← citeshttps://doi.org/10.1186/s12913-024-11932-xopenalex-snapshot
Exploring machine learning algorithms to predict not using modern family planning methods among reproductive age women in East Africa
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1010932openalex-snapshot
Robust deep learning object recognition models rely on low frequency information in natural images
inferred← citeshttps://doi.org/10.14429/dsj.68.12371openalex-snapshot
A Survey of Adversarial Machine Learning in Cyber Warfare
inferred← citeshttps://doi.org/10.14722/ndss.2018.23198openalex-snapshot
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
inferred← citeshttps://doi.org/10.1609/aaai.v34i04.6140openalex-snapshot
ML-LOO: Detecting Adversarial Examples with Feature Attribution
inferred← citeshttps://doi.org/10.1613/jair.1.13482openalex-snapshot
Efficient Learning of Interpretable Classification Rules
inferred← citeshttps://doi.org/10.17760/d20467286openalex-snapshot
Towards robust and secure deep learning models and beyond
inferred← citeshttps://doi.org/10.21203/rs.3.rs-1972947/v1openalex-snapshot
Adversarial Robustness in Deep Neural Networks based on Variable Attributes Stochastic Ensemble Model
inferred← citeshttps://doi.org/10.26686/wgtn.16655416openalex-snapshot
Lateralized Learning to Solve Complex Problems
inferred← citeshttps://doi.org/10.26686/wgtn.16655416.v1openalex-snapshot
Lateralized Learning to Solve Complex Problems
inferred← citeshttps://doi.org/10.3103/s0146411623080072openalex-snapshot
Hybrid Method for the Detection of Evasion Attacks Aimed at Machine Learning Systems
inferred← citeshttps://doi.org/10.31979/etd.wfjb-2m4yopenalex-snapshot
Linear Inverse Problems and Neural Networks
inferred← citeshttps://doi.org/10.32657/10356/146731openalex-snapshot
Fault detection method based on high-level feedback and reconstruction for image recognition
inferred← citeshttps://doi.org/10.32657/10356/152267openalex-snapshot
Securing edge deep neural network against input evasion and IP theft
inferred← citeshttps://doi.org/10.32664/j-intech.v11i1.845openalex-snapshot
Pemanfaatan Deep Convolutional Auto-encoder untuk Mitigasi Serangan Adversarial Attack pada Citra Digital
inferred← citeshttps://doi.org/10.3390/app12084054openalex-snapshot
A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions
inferred← citeshttps://doi.org/10.3390/electronics11121814openalex-snapshot
Defending Adversarial Attacks against DNN Image Classification Models by a Noise-Fusion Method
inferred← citeshttps://doi.org/10.3390/en17020416openalex-snapshot
A Solar and Wind Energy Evaluation Methodology Using Artificial Intelligence Technologies
inferred← citeshttps://doi.org/10.3390/fi15020062openalex-snapshot
Adversarial Machine Learning Attacks against Intrusion Detection Systems: A Survey on Strategies and Defense
inferred← citeshttps://doi.org/10.36227/techrxiv.24043479openalex-snapshot
Out2In: Towards Machine Learning Models Resilient to Adversarial and Natural Distribution Shifts
inferred← citeshttps://doi.org/10.36227/techrxiv.24043479.v1openalex-snapshot
Out2In: Towards Machine Learning Models Resilient to Adversarial and Natural Distribution Shifts
inferred← citeshttps://doi.org/10.46632/jdaai/2/3/1openalex-snapshot
Artificial Intelligence Approaches and Mechanisms for Big Data Analytics: A Systematic Study
inferred← citeshttps://doi.org/10.48550/arxiv.1705.02900openalex-snapshot
Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression
inferred← citeshttps://doi.org/10.48550/arxiv.1706.00633openalex-snapshot
Towards Robust Detection of Adversarial Examples
inferred← citeshttps://doi.org/10.48550/arxiv.1710.00814openalex-snapshot
Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight
inferred← citeshttps://doi.org/10.48550/arxiv.1712.07107openalex-snapshot
Adversarial Examples: Attacks and Defenses for Deep Learning
inferred← citeshttps://doi.org/10.48550/arxiv.1801.00553openalex-snapshot
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
inferred← citeshttps://doi.org/10.48550/arxiv.1802.01549openalex-snapshot
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples
inferred← citeshttps://doi.org/10.48550/arxiv.1802.06806openalex-snapshot
Divide, Denoise, and Defend against Adversarial Attacks
inferred← citeshttps://doi.org/10.48550/arxiv.1802.06816openalex-snapshot
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression
inferred← citeshttps://doi.org/10.48550/arxiv.1803.03880openalex-snapshot
Combating Adversarial Attacks Using Sparse Representations
inferred← citeshttps://doi.org/10.48550/arxiv.1803.10840openalex-snapshot
Defending against Adversarial Images using Basis Functions Transformations
inferred← citeshttps://doi.org/10.48550/arxiv.1806.01471openalex-snapshot
PAC-learning in the presence of evasion adversaries
inferred← citeshttps://doi.org/10.48550/arxiv.1806.01477openalex-snapshot
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks
inferred← citeshttps://doi.org/10.48550/arxiv.1806.11186openalex-snapshot
A New Angle on L2 Regularization
inferred← citeshttps://doi.org/10.48550/arxiv.1807.06732openalex-snapshot
Motivating the Rules of the Game for Adversarial Example Research
inferred← citeshttps://doi.org/10.48550/arxiv.1808.05705openalex-snapshot
Mitigation of Adversarial Attacks through Embedded Feature Selection
inferred← citeshttps://doi.org/10.48550/arxiv.1810.01185openalex-snapshot
Adversarial Examples - A Complete Characterisation of the Phenomenon
inferred← citeshttps://doi.org/10.48550/arxiv.1810.10625openalex-snapshot
Robust Adversarial Learning via Sparsifying Front Ends
inferred← citeshttps://doi.org/10.48550/arxiv.1811.03571openalex-snapshot
Intrinsic Geometric Vulnerability of High-Dimensional Artificial Intelligence
inferred← citeshttps://doi.org/10.48550/arxiv.1812.01713openalex-snapshot
FineFool: Fine Object Contour Attack via Attention
inferred← citeshttps://doi.org/10.48550/arxiv.1812.09638openalex-snapshot
Exploiting the Inherent Limitation of L0 Adversarial Examples
inferred← citeshttps://doi.org/10.48550/arxiv.1901.09878openalex-snapshot
CapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule Networks
inferred← citeshttps://doi.org/10.48550/arxiv.1905.01726openalex-snapshot
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples
inferred← citeshttps://doi.org/10.48550/arxiv.1905.13148openalex-snapshot
Moving Target Defense for Deep Visual Sensing against Adversarial Examples
inferred← citeshttps://doi.org/10.48550/arxiv.1906.05599openalex-snapshot
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks
inferred← citeshttps://doi.org/10.48550/arxiv.1908.02658openalex-snapshot
Random Directional Attack for Fooling Deep Neural Networks
inferred← citeshttps://doi.org/10.48550/arxiv.1911.11484openalex-snapshot
Using Depth for Pixel-Wise Detection of Adversarial Attacks in Crowd Counting
inferred← citeshttps://doi.org/10.48550/arxiv.2001.00308openalex-snapshot
ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial Defense
inferred← citeshttps://doi.org/10.48550/arxiv.2001.09486openalex-snapshot
Ensemble Noise Simulation to Handle Uncertainty about Gradient-based Adversarial Attacks
inferred← citeshttps://doi.org/10.48550/arxiv.2003.00883openalex-snapshot
Adversarial Perturbations Prevail in the Y-Channel of the YCbCr Color Space
inferred← citeshttps://doi.org/10.48550/arxiv.2003.07573openalex-snapshot
Heat and Blur: An Effective and Fast Defense Against Adversarial Examples
inferred← citeshttps://doi.org/10.48550/arxiv.2003.07982openalex-snapshot
Adversarial Transferability in Wearable Sensor Systems
inferred← citeshttps://doi.org/10.48550/arxiv.2003.12760openalex-snapshot
Adversarial Imitation Attack
inferred← citeshttps://doi.org/10.48550/arxiv.2006.14536openalex-snapshot
Smooth Adversarial Training
inferred← citeshttps://doi.org/10.48550/arxiv.2007.00711openalex-snapshot
ConFoc: Content-Focus Protection Against Trojan Attacks on Neural Networks
inferred← citeshttps://doi.org/10.48550/arxiv.2007.01356openalex-snapshot
Decoder-free Robustness Disentanglement without (Additional) Supervision
inferred← citeshttps://doi.org/10.48550/arxiv.2008.02076openalex-snapshot
Attacking and Defending Machine Learning Applications of Public Cloud
inferred← citeshttps://doi.org/10.48550/arxiv.2008.04094openalex-snapshot
Adversarial Examples on Object Recognition: A Comprehensive Survey
inferred← citeshttps://doi.org/10.48550/arxiv.2010.11143openalex-snapshot
Progressive Defense Against Adversarial Attacks for Deep Learning as a Service in Internet of Things
inferred← citeshttps://doi.org/10.48550/arxiv.2012.09110openalex-snapshot
Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges
inferred← citeshttps://doi.org/10.48550/arxiv.2103.06487openalex-snapshot
DAFAR: Defending against Adversaries by Feedback-Autoencoder Reconstruction
inferred← citeshttps://doi.org/10.48550/arxiv.2103.11526openalex-snapshot
ExAD: An Ensemble Approach for Explanation-based Adversarial Detection
inferred← citeshttps://doi.org/10.48550/arxiv.2104.01494openalex-snapshot
Mitigating Gradient-based Adversarial Attacks via Denoising and Compression
inferred← citeshttps://doi.org/10.48550/arxiv.2107.00561openalex-snapshot
Using Anomaly Feature Vectors for Detecting, Classifying and Warning of Outlier Adversarial Examples
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