Defensive Distillation is Not Robust to Adversarial Examples
arXiv:1607.04311
Abstract
We show that defensive distillation is not secure: it is no more resistant to targeted misclassification attacks than unprotected neural networks.
Cited by in corpus (44)
- On Evaluating Adversarial Robustness
- Fast is better than free: Revisiting adversarial training
- NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles
- Adversarial Examples that Fool Detectors
- TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems
- Adversarial Examples in Modern Machine Learning: A Review
- Standard detectors aren't (currently) fooled by physical adversarial stop signs
- Assessing Threat of Adversarial Examples on Deep Neural Networks
- Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks
- RAID: Randomized Adversarial-Input Detection for Neural Networks
- Online Robustness Training for Deep Reinforcement Learning
- Building Robust Deep Neural Networks for Road Sign Detection
- Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks
- Image Transformation can make Neural Networks more robust against Adversarial Examples
- ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds
- Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial Training
- Regula Sub-rosa: Latent Backdoor Attacks on Deep Neural Networks
- Robustness Certificates Against Adversarial Examples for ReLU Networks
- Trust but Verify: An Information-Theoretic Explanation for the Adversarial Fragility of Machine Learning Systems, and a General Defense against Adversarial Attacks
- Bandlimiting Neural Networks Against Adversarial Attacks
- Minimax Defense against Gradient-based Adversarial Attacks
- FAT: Federated Adversarial Training
- Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks
- Efficient detection of adversarial images
- Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks
- On the human-recognizability phenomenon of adversarially trained deep image classifiers
- Non-Determinism in Neural Networks for Adversarial Robustness
- Controlled Caption Generation for Images Through Adversarial Attacks
- Who is Responsible for Adversarial Defense?
- Ensemble Generative Cleaning with Feedback Loops for Defending Adversarial Attacks
- A Useful Taxonomy for Adversarial Robustness of Neural Networks
- An Information-Theoretic Explanation for the Adversarial Fragility of AI Classifiers
- An Empirical Study of DNNs Robustification Inefficacy in Protecting Visual Recommenders
- Attack to Fool and Explain Deep Networks
- Rethinking Uncertainty in Deep Learning: Whether and How it Improves Robustness
- Delving into the pixels of adversarial samples
- Strategies to architect AI Safety: Defense to guard AI from Adversaries
- A New Family of Neural Networks Provably Resistant to Adversarial Attacks
- Orthogonal Deep Models As Defense Against Black-Box Attacks
- Tricking Adversarial Attacks To Fail
- Enhancing Resilience of Deep Learning Networks by Means of Transferable Adversaries
- Can Intelligent Hyperparameter Selection Improve Resistance to Adversarial Examples?
- Target Training Does Adversarial Training Without Adversarial Samples
- Group-Structured Adversarial Training