Merge branch 'master' into single_cmd_download

CODEOWNERS

@@ -1,5 +1,6 @@
 adversarial_crypto/* @dave-andersen
 adversarial_text/* @rsepassi
+adv_imagenet_models/* @AlexeyKurakin
 attention_ocr/* @alexgorban
 audioset/* @plakal @dpwe
 autoencoders/* @snurkabill

adv_imagenet_models/README.md

@@ -0,0 +1,86 @@
+# Adversarially trained ImageNet models
+
+Pre-trained ImageNet models from the following papers:
+
+* [Adversarial Machine Learning at Scale](https://arxiv.org/abs/1611.01236)
+* [Ensemble Adversarial Training: Attacks and Defenses](https://arxiv.org/abs/1705.07204)
+
+## Contact
+
+Author: Alexey Kurakin,
+github: [AlexeyKurakin](https://github.com/AlexeyKurakin)
+
+## Pre-requesites and installation
+
+Ensure that you have installed TensorFlow 1.1 or greater
+([instructions](https://www.tensorflow.org/install/)).
+
+You also need copy of ImageNet dataset if you want to run provided example.
+Follow
+[Preparing the dataset](https://github.com/tensorflow/models/tree/master/slim#Data)
+instructions in TF-Slim library to get and preprocess ImageNet data.
+
+## Available models
+
+Following pre-trained models are available:
+
+Network Architecture | Adversarial training | Checkpoint
+---------------------|----------------------|----------------
+Inception v3 | Step L.L. | [adv_inception_v3_2017_08_18.tar.gz](http://download.tensorflow.org/models/adv_inception_v3_2017_08_18.tar.gz)
+Inception v3 | Step L.L. on ensemble of 3 models | [ens3_adv_inception_v3_2017_08_18.tar.gz](http://download.tensorflow.org/models/ens3_adv_inception_v3_2017_08_18.tar.gz)
+Inception v3 | Step L.L. on ensemble of 4 models| [ens4_adv_inception_v3_2017_08_18.tar.gz](http://download.tensorflow.org/models/ens4_adv_inception_v3_2017_08_18.tar.gz)
+Inception ResNet v2 | Step L.L. on ensemble of 3 models | [ens_adv_inception_resnet_v2_2017_08_18.tar.gz](http://download.tensorflow.org/models/ens_adv_inception_resnet_v2_2017_08_18.tar.gz)
+
+All checkpoints are compatible with
+[TF-Slim](https://github.com/tensorflow/models/tree/master/slim)
+implementation of Inception v3 and Inception Resnet v2.
+
+## How to evaluate models on ImageNet test data
+
+Python script `eval_on_adversarial.py` allow you to evaluate provided models
+on white-box adversarial examples generated from ImageNet test set.
+
+Usage is following:
+
+```bash
+# ${MODEL_NAME} - type of network architecture,
+#     either "inception_v3" or "inception_resnet_v2"
+# ${CHECKPOINT_PATH} - path to model checkpoint
+# ${DATASET_DIR} - directory with ImageNet test set
+# ${ADV_METHOD} - which method to use to generate adversarial images,
+#   supported method:
+#     "none" - use clean images from the dataset
+#     "stepll" - one step towards least likely class method (StepLL),
+#         see https://arxiv.org/abs/1611.01236 for details
+#     "stepllnoise" - RAND+StepLL method from https://arxiv.org/abs/1705.07204
+# ${ADV_EPS} - size of adversarial perturbation, ignored when method is none
+python eval_on_adversarial.py \
+  --model_name=${MODEL_NAME} \
+  --checkpoint_path=${CHECKPOINT_PATH} \
+  --dataset_dir=${DATASET_DIR} \
+  --batch_size=50 \
+  --adversarial_method=${ADV_METHOD} \
+  --adversarial_eps=${ADV_EPS}
+```
+
+Below is an example how to evaluate one of the models on RAND+StepLL adversarial
+examples:
+
+```bash
+# Download checkpoint
+CHECKPOINT_DIR=/tmp/checkpoints
+mkdir ${CHECKPOINT_DIR}
+wget http://download.tensorflow.org/models/ens_adv_inception_resnet_v2_2017_08_18.tar.gz
+tar -xvf ens_adv_inception_resnet_v2_2017_08_18.tar.gz
+mv ens_adv_inception_resnet_v2.ckpt* ${CHECKPOINT_DIR}
+rm ens_adv_inception_resnet_v2_2017_08_18.tar.gz
+
+# Run evaluation
+python eval_on_adversarial.py \
+  --model_name=inception_v3 \
+  --checkpoint_path=${CHECKPOINT_DIR}/ens_adv_inception_resnet_v2.ckpt \
+  --dataset_dir=${DATASET_DIR} \
+  --batch_size=50 \
+  --adversarial_method=stepllnoise \
+  --adversarial_eps=16
+```