awesome-adversarial-examples-dl
github.com/chbrian/awesome-adversarial-examples-dl ↗A curated list of awesome resources for adversarial examples in deep learning
266
GitHub Stars
84
Curated Resources
7
Categories
4 hours ago
Last Refreshed
Adversarial Examples for Machine LearningApproaches for Generating Adversarial Examples in Deep LearningDefenses for Adversarial ExamplesApplications for Adversarial ExamplesTransferability of Adversarial ExamplesAnalysis of Adversarial ExamplesTools
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me semantic segmentation resources from awesome-adversarial-examples-dl"
Installation instructions →What's inside
Analysis of Adversarial Examples
Applications for Adversarial Examples
- Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognitionSemantic Segmentation
- Adaptive Adversarial Attack on Scene Text RecognitionScene Text Recognition
- Adversarial attacks on neural network policiesReinforcement Learning
- Adversarial examples for evaluating reading comprehension systemsReading Comprehension
- Adversarial examples for generative modelsGenerative Modelling
- Adversarial examples for malware detectionMalware Detection
Defenses for Adversarial Examples
- Adversarial and Clean Data Are Not TwinsAdversarial Detecting
- Adversarial Example Defenses: Ensembles of Weak Defenses are not StrongOthers
- Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection MethodsOthers
- Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep NetworksClassifier Robustifying
- Adversarial Logit PairingAdversarial (Re)Training
- Adversarial machine learning at scaleAdversarial (Re)Training
Approaches for Generating Adversarial Examples in Deep Learning
- Adversarial Attacks and Defences Competition
- Adversarial diversity and hard positive generation
- Adversarial examples in the physical world
- Adversarial manipulation of deep representations
- Deepfool: a simple and accurate method to fool deep neural networks
- Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Adversarial Examples for Machine Learning
Tools
Showing a sample of 84 resources. View the full list on GitHub →