Dr. Alfonso Muñoz - Head of Cybersecurity lab (@mindcrypt)
D. José Ignacio Escribano - Security & Machine Learning Researcher (joseignacio.escribano@i4s.com)
Cybersecurity Lab - Innovation 4 Security (BBVA group)
- Learning from Simulated and Unsupervised Images through Adversarial Training
- Steganographic Generative Adversarial Networks
- Learning to Protect Communications with Adversarial Neural Cryptography
- SSGAN: Secure Steganography Based on Generative Adversarial Networks
- Generating steganographic images via adversarial training
- CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy
- Privacy-Preserving Classification on Deep Neural Network
- Deep Learning Adversarial Examples – Clarifying Misconceptions
- Awesome Adversarial Machine Learning
- Introduction to Adversarial Machine Learning
- Explaining and Harnessing Adversarial Examples
- The Limitations of Deep Learning in Adversarial Settings
- Practical Black-Box Attacks against Machine Learning
- BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
- PassGAN: A Deep Learning Approach for Password Guessing
- Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition
- Stealing Machine Learning Models via Prediction APIs
- Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains
- Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
- Universal adversarial perturbations
- Adversarial examples in the physical world
- Poisoning attacks against SVMs
- A Neural Network Playground - TensorFlow
- TensorFire
- Transferencia de estilo
- Generación de contraseñas con Keras
- Criptografía
- Esteganografía
- Cleverhans
- Deep Pwning
- Adversarial Steganography
- Neural Cryptography Tensorflow
- Adversarial Neural Crypt
- Deep Pwning
- Cleverhans
- Generación de contraseñas
- Transferencia de estilo
- Machine Learning and Computer Security Workshop
- ACM Workshop on Artificial Intelligence and Security
- KAPSARC Data Portal
- Datasets | Kaggle
- US Government Web Services and XML Data Sources
- AWS Public Datasets
- Registry of Research Data Repositories
- Reddit datasets
- Samples of Security Related Data
- SecLists
- List of datasets for machine learning research
- THE MNIST DATABASE of handwritten digits
- CIFAR-10 and CIFAR-100 datasets
- Large-scale CelebFaces Attributes (CelebA) Dataset
- ImageNet
- COCO - Common Objects in Context
- scikit-learn: Machine Learning in Python
- H2O.ai
- MLlib | Apache Spark
- Weka
- Apache Mahout: Scalable machine learning and data mining
- TensorFlow
- Caffe 2 | A New Lightweight, Modular, and Scalable Deep Learning Framework
- Torch
- Theano
- Keras
- Inteligencia artificial en AWS. Aprendizaje automático potente para desarrolladores y científicos de datos
- TensorFlow en AWS – Aprendizaje profundo en la nube
- Amazon Machine Learning - Predictive Analytics with AWS
- Google Cloud Machine Learning at Scale | Google Cloud Platform
- Cloud Machine Learning Engine - Google Cloud Platform
- Machine Learning | Microsoft Azure
- FloydHub - Deep Learning Platform - Cloud GPU
- BigML.com is Machine Learning made easy
- Moral Machine
- RoboLaw
- Artificial Intelligence Safety and Cybersecurity: a Timeline of AI Failures
- An Open Letter RESEARCH PRIORITIES FOR ROBUST AND BENEFICIAL ARTIFICIAL INTELLIGENCE
- Microsoft deletes 'teen girl' AI after it became a Hitler-loving sex robot within 24 hours
- “Racist” Camera Phenomenon Explained — Almost
- Google Apologizes For Tagging Photos Of Black People As ‘Gorillas’
- When Will AI Exceed Human Performance? Evidence from AI Experts
- Killer robots? Musk and Zuckerberg escalate row over dangers of AI
- Researchers warn against the rise of "big data hubris"
- Ciencia de datos o augurio: El método científico en la era del Big Data
- Artículo 22 UE RGDP "Decisiones individuales automatizadas, incluida la elaboración de perfiles"
- THE NEURAL NETWORK ZOO