@@ -7,23 +7,32 @@ The library is still under development. Feedback, bug reports and extensions are
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88## Supported attack and defense methods
99
10- The library contains implementations of the following attacks:
10+ The library contains implementations of the following ** evasion attacks** :
1111* DeepFool ([ Moosavi-Dezfooli et al., 2015] ( https://arxiv.org/abs/1511.04599 ) )
1212* Fast Gradient Method ([ Goodfellow et al., 2014] ( https://arxiv.org/abs/1412.6572 ) )
1313* Basic Iterative Method ([ Kurakin et al., 2016] ( https://arxiv.org/abs/1607.02533 ) )
14+ * Projected Gradient Descent ([ Madry et al., 2017] ( https://arxiv.org/abs/1706.06083 ) )
1415* Jacobian Saliency Map ([ Papernot et al., 2016] ( https://arxiv.org/abs/1511.07528 ) )
1516* Universal Perturbation ([ Moosavi-Dezfooli et al., 2016] ( https://arxiv.org/abs/1610.08401 ) )
1617* Virtual Adversarial Method ([ Miyato et al., 2015] ( https://arxiv.org/abs/1507.00677 ) )
1718* C& ; W Attack ([ Carlini and Wagner, 2016] ( https://arxiv.org/abs/1608.04644 ) )
1819* NewtonFool ([ Jang et al., 2017] ( http://doi.acm.org/10.1145/3134600.3134635 ) )
1920
20- The following defense methods are also supported:
21+ The following ** defence ** methods are also supported:
2122* Feature squeezing ([ Xu et al., 2017] ( http://arxiv.org/abs/1704.01155 ) )
2223* Spatial smoothing ([ Xu et al., 2017] ( http://arxiv.org/abs/1704.01155 ) )
2324* Label smoothing ([ Warde-Farley and Goodfellow, 2016] ( https://pdfs.semanticscholar.org/b5ec/486044c6218dd41b17d8bba502b32a12b91a.pdf ) )
2425* Adversarial training ([ Szegedy et al., 2013] ( http://arxiv.org/abs/1312.6199 ) )
2526* Virtual adversarial training ([ Miyato et al., 2015] ( https://arxiv.org/abs/1507.00677 ) )
2627* Gaussian data augmentation ([ Zantedeschi et al., 2017] ( https://arxiv.org/abs/1707.06728 ) )
28+ * Thermometer encoding ([ Buckman et al., 2018] ( https://openreview.net/forum?id=S18Su--CW ) )
29+
30+ ART also implements ** detection** methods of adversarial samples:
31+ * Basic detector based on inputs
32+ * Detector trained on the activations of a specific layer
33+
34+ The following ** detector of poisoning attacks** is also supported:
35+ * Detector based on activations analysis
2736
2837## Setup
2938
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