beecode1001/unmask

Adversarial detection and defense for deep learning systems using robust feature alignment

UnMask: Adversarial Detection and Defense in Deep Learning Through Building-Block Knowledge Extraction

Accepted to IEEE Big Data 2021. Full paper can be found here: https://arxiv.org/pdf/2002.09576.pdf

Overview:

To combat adversarial attacks on deep learning models, we developed UnMask, a knowledge-based adversarial detection and defense framework. The core idea behind UnMask is to protect vulnerable deep learning models by verifying that an image's predicted class ("bird") contains the expected building blocks (e.g., beak, wings, eyes). For example, if an image is classified as "bird", but the extracted building-blocks are wheel, seat and frame, we can utilize this information to alert of an potential attack and re-classify the image based on it's building blocks.

Extensive evaluation shows that UnMask (1) detects up to 96.75% of attacks, with a false positive rate of 9.66% and (2) defends the model by correctly classifying up to 93% of adversarial images produced by the current strongest attack, Projected Gradient Descent, in the gray-box setting. In addition, UnMask performs 31.18% better than adversarial training when averaged across 8 attack vectors. Our proposed method is architecture agnostic and fast.

Setup:

We run the experiments in a Linux environment using Python 3, on 2 Nvidia Titan X's, a Titan RTX and an Nvidia DGX. We note that a single Nvidia GPU is sufficient to recreate all of the experiments, however, we recommend that the GPU has at least 12GB of memory (not tested with less). However, be believe that that less GPU memory should work without issues.

We utilize Anaconda 3 to manage all of the Python packages. To facilitate reproducibility of the Python environment, we release an Anaconda YAML specification file of the libraries utilized in the experiments. This allows the user to create a new virtual Python environment with all of the packages required to run the code by importing the YAML file. In addition, we utilize two Github repositories in the code base---(i) for the mask R-CNN model and (ii) to assist in the development of the dataset. Since there are significant modifications from the original implementation, it's necessary for the user to utilize the provided version.

(i) https://github.com/matterport/Mask_RCNN

(ii) https://github.com/waspinator/pycococreator

UnMask Dataset:

We contribute a new dataset incorporating four components--PASCAL-Part, PASCAL VOC 2010, a subset of ImageNet and images scraped from Flickr. The goal of this curation is to (i) collect all of the data used in our evaluation as a single source to promote ease of reproducibility by our research community, and (ii) to increase the number of images available for evaluating the performance of the deep learning models and the UnMask defense framework. We designed multiple class sets with varying number of classes and feature overlap (e.g., CS3a) to study how they would affect detection and defense effectiveness.

Models:

There are two models that we train and evaluate--(1) the deep learning model we are protecting (M) and (2) the building-block knowledge extraction model (K).

Model M: To train each model M, we utilize a subset of the Pascal VOC 2010 dataset, taking only images containing a single object (no multi-class images); and a subset of the images from ImageNet. The total number of images that each model M utilizes for training, validation and testing depends on the class set. Additional information, can be found in the paper.

Model K: To train the feature extraction model K, we utilize the Pascal Part dataset containing 9,323 images for training, validation and testing, with ratios of 80/10/10, respectively. We follow a similar training and evaluation procedure described in (https://github.com/matterport/Mask_RCNN) training for 40 epochs with basic data augmentation. To the best of our knowledge this is the first mask R-CNN model developed to work with the Pascal Part dataset.

In the Dropbox link below we provide 2 models, one of model M and one of Model K.

Download the dataset and models:

In order to run the code, download and unzip the "data" folder inside the main code directory. The dataset folder can be downloaded from here.

In addition, download the two files mask_rcnn_coco.h5 and mask_rcnn_parts_0040.h5 from here and here, respectively. Place the mask_rcnn_parts_0040.h5 file inside the Mask_RCNN/logs/model_k folder and place mask_rcnn.coco.h5 inside Mask_RCNN folder.

Running the code:

Environment Setup:

    1. Setup a Linux environment (not tested for Windows) with an Nvidia GPU containing at least 12GB of memory (less may work, but not tested).
    2. Download the open-sourced code, dataset and models from Github.
    3. Create a virtual Python environment using the provided YAML configuration file on Github.
    4. Activate the new virtual Python environment

Parameters:

• Model(s): the user can select between a few models. [ResNet50, DenseNet121]

• Class set: the class set to utilize when evaluating UnMask. [cs3-1, cs3-2, cs5-1, cs5-2]

Running UnMask: In order to run UnMask, run the main.py file. Parameter options can be set in config.py.