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awesome-adversarial-examples-dl
A curated list of awesome resources for adversarial examples in deep learning
https://github.com/chbrian/awesome-adversarial-examples-dl
- The security of machine learning - 148.
- Adversarial classification
- Adversarial learning
- Multiple classifier systems for robust classifier design in adversarial environments - 4 (2010): 27-41.
- Evasion Attacks against Machine Learning at Test Time
- Can machine learning be secure?
- Towards the science of security and privacy in machine learning
- Pattern recognition systems under attack
- Intriguing properties of neural networks
- Explaining and harnessing adversarial examples
- Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
- Adversarial examples in the physical world
- Adversarial diversity and hard positive generation
- The limitations of deep learning in adversarial settings
- Adversarial manipulation of deep representations
- Deepfool: a simple and accurate method to fool deep neural networks - Dezfooli, Seyed-Mohsen, Alhussein Fawzi, and Pascal Frossard. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
- Universal adversarial perturbations - Dezfooli, Seyed-Mohsen, et al. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017.
- Towards evaluating the robustness of neural networks
- Machine Learning as an Adversarial Service: Learning Black-Box Adversarial Examples
- Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models - Yu, et al. 10th ACM Workshop on Artificial Intelligence and Security (AISEC) with the 24th ACM Conference on Computer and Communications Security (CCS). 2017.
- Ground-Truth Adversarial Examples
- Generating Natural Adversarial Examples
- Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
- Adversarial Attacks and Defences Competition
- Distillation as a defense to adversarial perturbations against deep neural networks
- Learning with a strong adversary
- Adversarial machine learning at scale
- Ensemble Adversarial Training: Attacks and Defenses
- Adversarial training for relation extraction
- Adversarial Logit Pairing
- Detecting Adversarial Samples from Artifacts
- Adversarial and Clean Data Are Not Twins - Shinn Ku. arXiv preprint arXiv:1704.04960 (2017).
- Safetynet: Detecting and rejecting adversarial examples robustly
- On the (statistical) detection of adversarial examples
- On detecting adversarial perturbations
- Early Methods for Detecting Adversarial Images
- Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers
- Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight - Chen, et al. arXiv preprint arXiv:1710.00814 (2017).
- PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
- PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples
- MagNet: a Two-Pronged Defense against Adversarial Examples
- Towards deep neural network architectures robust to adversarial examples
- Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks
- Robustness to Adversarial Examples through an Ensemble of Specialists
- Reluplex: An efficient SMT solver for verifying deep neural networks
- Safety verification of deep neural networks
- Towards proving the adversarial robustness of deep neural networks
- Deepsafe: A data-driven approach for checking adversarial robustness in neural networks
- DeepXplore: Automated Whitebox Testing of Deep Learning Systems
- Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong
- Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
- Adversarial attacks on neural network policies
- Delving into adversarial attacks on deep policies
- Adversarial examples for generative models
- Adversarial images for variational autoencoders
- Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
- Adversarial Examples for Semantic Segmentation and Object Detection
- Adversarial Examples for Semantic Image Segmentation
- Universal Adversarial Perturbations Against Semantic Image Segmentation
- Semantic Image Synthesis via Adversarial Learning
- Adversarial Examples for Semantic Segmentation and Object Detection
- Physical Adversarial Examples for Object Detectors
- Adaptive Adversarial Attack on Scene Text Recognition
- Adversarial examples for evaluating reading comprehension systems
- Understanding Neural Networks through Representation Erasure
- Adversarial examples for malware detection
- Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
- Evading Machine Learning Malware Detection
- DeepDGA: Adversarially-Tuned Domain Generation and Detection
- Automatically evading classifiers
- Targeted Adversarial Examples for Black Box Audio Systems
- CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition
- Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
- Transferability in machine learning: from phenomena to black-box attacks using adversarial samples
- Delving into transferable adversarial examples and black-box attacks
- Fundamental limits on adversarial robustness
- Exploring the space of adversarial images
- A boundary tilting perspective on the phenomenon of adversarial examples
- Measuring neural net robustness with constraints
- Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples
- Adversarially Robust Generalization Requires More Data
- A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples
- Adversarial vulnerability for any classifier
- Adversarial Spheres
- cleverhans v2.0.0: an adversarial machine learning library
- Foolbox: A Python toolbox to benchmark the robustness of machine learning models
- advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch