Merantix-Labs: DAAIN
This is the code for our paper DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows which can be found at arxiv.
Assumptions
There are assumptions:
- The training data PerturbedDataset makes some assumptions about the data:
- the
ignore_indexis 255 num_classes= 19- the images are resized with
size == 512
- the
Module Overview
A selection of the files with some pointers what to find where
├── configs # The yaml configs
│ ├── activation_spaces
│ │ └── esp_net_256_512.yaml
│ ├── backbone
│ │ ├── esp_dropout.yaml
│ │ └── esp_net.yaml
│ ├── dataset_paths
│ │ ├── bdd100k.yaml
│ │ └── cityscapes.yaml
│ ├── data_creation.yaml # Used to create the training and testing data in one go
│ ├── detection_inference.yaml # Used for inference
│ ├── detection_training.yaml # Used for training
│ ├── esp_dropout_training.yaml # Used to train the MC dropout baseline
│ └── paths.yaml
├── README.md # This file!
├── requirements.in # The requirements
├── setup.py
└── src
└── daain
├── backbones # Definitions of the backbones, currently only a slighlty modified version
│ │ # of the ESPNet was tested
│ ├── esp_dropout_net
│ │ ├── esp_dropout_net.py
│ │ ├── __init__.py
│ │ ├── lightning_module.py
│ │ └── trainer
│ │ ├── criteria.py
│ │ ├── data.py
│ │ ├── dataset_collate.py
│ │ ├── data_statistics.py
│ │ ├── __init__.py
│ │ ├── iou_eval.py
│ │ ├── README.md
│ │ ├── trainer.py # launch this file to train the ESPDropoutNet
│ │ ├── transformations.py
│ │ └── visualize_graph.py
│ └── esp_net
│ ├── espnet.py # Definition of the CustomESPNet
│ └── layers.py
├── baseline
│ ├── maximum_softmax_probability.py
│ ├── max_logit.py
│ └── monte_carlo_dropout.py
├── config_schema
├── constants.py # Some constants, the last thing to refactor...
├── data # General data classes
│ ├── datasets
│ │ ├── bdd100k_dataset.py
│ │ ├── cityscapes_dataset.py
│ │ ├── labels
│ │ │ ├── bdd100k.py
│ │ │ ├── cityscape.py
│ │ └── semantic_segmentation_dataset.py
│ ├── activations_dataset.py # This class loads the recorded activations
│ └── perturbed_dataset.py # This class loads the attacked images
├── model
│ ├── aggregation_mode.py # Not interesting for inference
│ ├── classifiers.py # All classifiers used are defined here
│ ├── model.py # Probably the most important module. Check this for an example on how
│ │ # to used the detection model and how to load the parts
│ │ # (normalising_flow & classifier)
│ └── normalising_flow
│ ├── coupling_blocks
│ │ ├── attention_blocks
│ │ ├── causal_coupling_bock.py # WIP
│ │ └── subnet_constructors.py
│ └── lightning_module.py
├── scripts
│ └── data_creation.py # Use this file to create the training and testing data
├── trainer # Trainer of the full detection model
│ ├── data.py # Loading the data...
│ └── trainer.py
├── utils # General utils
└── visualisations # Visualisation helpers
Parts
In general the model consists of two parts:
- Normalising FLow
- Classifier / Scoring method
Both have to be trained separately, depending on the classifier. Some are parameter free (except for the threshold).
The general idea can be summarised:
- Record the activations of the backbone model at specific locations during a forward pass.
- Transform the recorded activations using a normalising flow and map them to a standard Gaussian for each variable.
- Apply some simple (mostly distance based) classifier on the transformed activations to get the anomaly score.
Training & Inference Process
- Generate perturbed and adversarial images. We do not provide code for this step.
- Generate the activations using
src/daain/scripts/data_creation.py - Train the detection model using
src/daain/trainer/trainer.py - Use
src/daain/model/model.pyto load the trained model and use it to get the anomaly score (the probability that the input was anomalous).
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