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).
46 Nov 14, 2022
12 Sep 11, 2022
363 Jan 03, 2023
90 Dec 22, 2022
243 Dec 30, 2022
444 Dec 30, 2022
43 Mar 15, 2022
10 Jun 30, 2021
1.8k Dec 28, 2022
428 Nov 22, 2022
14 Sep 30, 2022
772 Dec 30, 2022
44 Dec 24, 2022
28 Nov 03, 2022
27 Dec 07, 2022
3.3k Dec 30, 2022
2 Jan 23, 2022
562 Jan 03, 2023
97 Dec 25, 2022
83 Jan 04, 2023