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Finished report and impact statements

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Adog64 2024-05-08 21:04:06 -04:00
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MIT License
Copyright (c) 2024 Aidan Sharpe
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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impact_statements/2024 - (Fillable) ECE Engineering Clinic - Impact and Professional Considerations Statement.pdf → impact_statements/IPCS_for_AidanSharpe_Enhancing_Image_Classifiers_with_Denoising_Filters_Polikar.pdf
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\documentclass{article}
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\usepackage[backend=biber]{biblatex}
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\title{Enhancing Image Classifiers with Denoising Filters}
\author{Aidan Sharpe}
\date{}
\begin{document}
\begin{titlepage}
\maketitle
\end{titlepage}
\newpage
\tableofcontents
\pagebreak
\begin{abstract}
With the sudden increase in machine learning applications brought forth in the last eighteen months or so, a new cybersecurity threat has been posed. Not only are traditional cloud-based threats a risk---denial of service attacks for example---AI services are also at risk of being sabotaged by adversarial examples. For this reason, standards have been passed by international governing bodies such as the IEEE to streamline the process of making such services more robust \cite{ieee3129}. To further support increased robustness for online AI services, we propose a pipeline that applies denoising filters to inputs before they reach the model. In doing so, the goal is to remove, or at least reduce the impact of, adversarial attacks. We find that in cases where data is particularly high contrast, some filters can maintain the classifying accuracy of the model at high attack strengths. However, in cases where the data is low contrast, the best filters are only able to keep accuracy slightly better than if the model was simply guessing classes at random.
\end{abstract}
\section{Introduction}
All neural networks are prone to adversarial attacks \cite{carlini2017evaluating} \cite{szegedy2014intriguing}. When it comes to defending against these attacks, one method is to train using both adversarial (attacked) and clean (not attacked) data. While this method has been shown to somewhat regularize the neural network \cite{goodfellow2015explaining}, the technique fails to generalize to other attacks. A defense technique, from which our approach draws inspiration, involves using a denoising autoencoder to produce only clean data from both adversarial and clean data. This technique shows promise and transferability across a range of attacks.
Unfortunately, this approach involves training a secondary model to protect the original, which can drastically increase the amount of computing power required for set up. Therefore, in scenarios where reducing computing power is a factor or a simple drop-in defense is required, we propose a pipeline in which commonly available denoising filters are used to pre-process inputs before being fed to the model.
By implementing this approach, we examined whether preprocessing filters are effective at defending adversarial attacks. To do so, we compared the efficacy of different filters at different filtering strengths. Finally, we plan to investigate the transferability of this approach across models and attacks.
\section{Approach and Experimental Timeline}
We began by establishing a baseline performance with an FGSM attack on a pre-trained MNIST CNN classifier. After verifying that the attack was working, the first filter could be implemented: a Gaussian Kuwahara filter. The Kuwahara filter was initially chosen because it excels at removing noise from smooth areas of an image while also preserving edges. Once this preliminary pipeline was up and running, the code was made more modular with a standard interface to facilitate swapping out models, attacks, and filters.
\begin{figure}[H]
\centering
\includegraphics[width=0.5\textwidth]{../poster/Concept.png}
\caption{Simple block diagram of the proposed pipeline}
\label{fig:concept-overview}
\end{figure}
With this interface constructed, all alternative filters were tested over a range of attack strengths and with a range of variation on each filter's free parameter. This free parameter is referred to as filtering "strength" for a lack of better terms, although some filters, such as bit-depth reduction, make larger alterations at lower "strength".
To prevent rerunning the program every time we wanted to change how results were visualized, data for each run was saved in the JSON format specified by the ECMA 404 standard \cite{ecma404}. This allowed for a companion program to read and display results in a number of ways.
Once we had results for MNIST, we shifted focus to implementing a CIFAR-10 classifier. Originally, we attempted to adapt the LeNet classifier we were already using. This was only able to achieve about 65\% to 70\% accuracy on the validation dataset before plateauing. Therefore, we decided to research what others had found success with. We started to test DLA \cite{yu2019deep}, but after taking too long to train, we pivoted again to VGG16, which we stuck with for testing on filters \cite{simonyan2014very}.
Once we had two working classifiers, LeNet trained on MNIST and VGG16 trained on CIFAR-10, we were able to compare the effectiveness of the filters across models.
The next logical progression, which will be implemented in the near future, is to implement another attack to evaluate the transferability of our approach across attack methods. This attack is likely to be Carlini and Wagner \cite{carlini2017evaluating}.
\section{Constraints}
In switching from LeNet to deeper networks, two constraints were posed. Firstly, the GPU in the machine used did not support any machine learning hardware acceleration tools like CUDA or ROCm. Being forced to do everything on the CPU imposed a limit on how much training and testing could be done within a reasonable amount of time. Limited computing resources also guided our decision to pick CIFAR-10 over a higher-resolution dataset like ImageNet.
The other constraint was posed by the version management system used. While Git itself does not really have a limit, the current server-side setup requires that files be under about 100 MB each. Since we wanted to store the trained models alongside the other files in the repository, this limited the number of parameters our models had. While the largest model used in testing was 62MiB, the limit significantly narrowed the list of possible choices.
\section{Alternative Filters}
Seven image processing filters were selected, and their performances were compared over a range of attack strengths, while parametrically varying each filter's free parameter. In this way, the optimal parameters for each filter could be found, and their effectiveness against adversarial examples could be examined. An overview of how each filter operates on a randomly selected MNIST example attacked with FGSM with $\epsilon=0.2$ is seen in figure \ref{fig:alternative_filters}.
\begin{figure}[H]
\centering
\includegraphics[width=0.125\textwidth]{../examples/unfiltered_eps_0.2.png}
\includegraphics[width=0.125\textwidth]{../examples/bit_depth_eps_0.2.png}
\includegraphics[width=0.125\textwidth]{../examples/gaussian_blur_eps_0.2.png}
\includegraphics[width=0.125\textwidth]{../examples/gaussian_kuwahara_eps_0.2.png}
\includegraphics[width=0.125\textwidth]{../examples/mean_kuwahara_eps_0.2.png}
\includegraphics[width=0.125\textwidth]{../examples/random_noise_eps_0.2.png}
\includegraphics[width=0.125\textwidth]{../examples/bilateral_filter_eps_0.2.png}
\includegraphics[width=0.125\textwidth]{../examples/threshold_eps_0.2.png}
\caption{The seven selected filters operating on a randomly selected sample from MNIST attacked with FGSM with $\epsilon=0.2$}
\label{fig:alternative_filters}
\end{figure}
The first set of filters examined were the Kuwahara filters, both Gaussian and Mean variants. They operate based on the variance of four overlapping regions in a kernel. These filters were selected due to their ability to preserve edges and blur smooth areas: a desirable property for removing noise. The Kuwahara filter is typically used to implement an oil painting effect in some software packages, but was originally used to make interpreting medical imaging easier \cite{kuwahara1976processing}. The free parameter for both Kuwahara filters is the radius of the kernel, and its relation to filter strength is given by:
\begin{equation}
r = 2s + 1
\label{eqn:rad_odd_strength}
\end{equation}
where $r$ is the kernel radius, and $s$ is the filter strength.
The Gaussian blur was implemented next, as it is a simple and very common filter. It acts as a lowpass filter, blurring both smooth areas and edges. Similar to the Kuwahara filters, the free parameter of the Gaussian blur is radius, and it has the same relationship to strength given by equation \ref{eqn:rad_odd_strength}.
The bilateral filter was selected because, like the Kuwahara filter, it reduces noise but preserves edges. The resulting images are very different, however. While the Kuwahara filter produces more "blotchy-looking" images, the bilateral filter produces smooth images. The bilateral filter instead uses kernel diameter as a free parameter, and the implementation used requires this parameter be an odd number. Hence, the diameter is calculated with the relationship:
\begin{equation}
d = 2s + 1
\end{equation}
where $d$ is the kernel diameter, and $s$ is the filtering strength.
As a control of sorts, to see if the filters were acting as defense or simply perturbing the attacked images away from the area of adversarial behavior, a noise "filter" was implemented. The noise added was specifically Gaussian noise with a mean $\mu=0$ and a standard deviation $\sigma=180$. The free parameter selected was the intensity of the noise, meaning with a higher strength, the signal to noise ratio (SNR) would degrade. The relationship between strength and intensity was
\begin{equation}
I = I_0 (2s + 1)
\end{equation}
where $I$ is intensity, $s$ is strength, and $I_0$ is the initial intensity. For our tests, we used $I_0 = 5\times10^{-4}$.
While not typically used as a filter, a bit-depth reduction was chosen because it quantizes the image. In doing so, small perturbations should have no change in most of the image, except for where the value was already close to the threshold. Overall, a bit-depth reduction should be solid against low-amplitude attacks. The free parameter of this operation is the number of bits. In relationship to strength,
\begin{equation}
b = 2^s
\end{equation}
where $b$ is the number of bits. The bit-depth reduction is one such case where increasing strength is produces counter-intuitive effects.
The final filter tested was a simple threshold. For each channel, the value would set to the maximum value when above the threshold and to the minimum value when below the threshold. For a floating-point greyscale image, the maximum value is one and the minimum value is zero. The threshold filter is another such filter where mapping the free parameter to strength seems odd. The free parameter is the threshold, which is given by
\begin{equation}
T = {2s + 1 \over 10}
\end{equation}
where $T$ is the threshold. Perhaps this definition could be better as it restricts strength to be values in the range $[-0.5,4.5]$. Regardless, this is the definition used in testing to get an even spread of thresholds between zero and one.
\section{Results and Evaluation}
With the two free parameters, filter strength and attack strength, the accuracy of each filter can be thought of as a point in 3D space. For each filter strength and attack strength, the highest accuracy filter was chosen to represent that point. For our purposes, the accuracy was defined as the ratio of correct predictions to total predictions. The best filters and their accuracies for each attack strength and filter strength for MNIST and CIFAR-10 are seen in figure \ref{fig:filter_performances}.
\begin{figure}[H]
\centering
\includegraphics[width=0.33\textwidth]{../results/MNIST_FGSM_3d_new.png}
\includegraphics[width=0.33\textwidth]{../results/CIFAR-10_FGSM_3d.png}
\caption{The highest rank filter and its accuracy for each attack strength and filter strength}
\label{fig:filter_performances}
\end{figure}
To determine how well a filter is performing, two measures of effectiveness are defined. An \emph{ideally effective} filter will produce the same classifier accuracy for both clean and attacked images. While a lofty goal for sure, this is the ideal case.
The second measure of effectiveness we defined is a \emph{minimally effective} filter. To be minimally effective, the accuracy of the classifier must only be higher than if it were randomly guessing the class. For example, for a balanced dataset with ten classes, the expected accuracy for randomly guessing is 10\%. If the accuracy is consistently above this threshold for both clean and attacked inputs, then the filter may be deemed minimally effective.
This definition was chosen, because if a classifier is performing better than randomly guessing, then it may be boosted to increase accuracy \cite{schapire1989strength}.
\section{Conclusions}
As seen in figure \ref{fig:filter_performances}, the threshold filter operates as a nearly ideal filter for MNIST even at the highest attack strengths tested. Additionally for the MNIST case, the Gaussian Kuwahara filter prevails at high filter strength and high attack strength.
For CIFAR-10, however, the results are not nearly as promising. While for every attack strength tested there was a filter at some strength that kept the classifier above the random guessing threshold, there were no filters that came even close to the ideal case.
We hypothesize that, at least for low-resolution datasets, having low contrast images makes defending attacks more difficult. Taking a look at what the threshold filter does to an attacked image in figure \ref{fig:alternative_filters}, it becomes apparent why it works so well for MNIST. Essentially, when attacked, the SNR is still quite low.
In the case of CIFAR-10, discerning the clean images can be difficult for a human observer at times. Therefore, it is understandable why FGSM impacted CIFAR-10 much more than MNIST. To better understand how filters apply to color photos in general, testing with the ImageNet database is a logical next step.
\section{Ethical Considerations}
\subsection{Heath and Safety Considerations}
When it comes to image classifier services, many tools will not have any direct impact on the health and safety of others. In some cases, however, if the classifier is being used to operate machinery, such as in self-driving vehicles, having a robust pipeline is important to keeping people out of harm's way. Additionally, for services such as plant classifiers, if a poisonous plant is identified as safe to eat, people may fall ill or perish.
Therefore, as such machine learning services become more popular, being robust to adversarial attacks is of the utmost importance to human health. For this reason, unless further research shows that filtering is an effective defense for higher resolution color images, we cannot recommend that filters be used as an adversarial defense in any service that may involve machinery, food, or any other potentially hazardous element.
We initially chose the filtering approach because it is potentially faster than a denoising autoencoder. This way, if our approach is found to be effective against adversarial attacks, we can increase the safety of equipment operators in time-sensitive operations.
\subsection{Social and Cultural Considerations}
Be it source code, experimental procedures, raw results, or otherwise, having closed-source anything in an academic project should raise a red flag. To ensure equal access and full transparency with the readers and those wishing to reproduce the results found in this paper, all code, results, document files, and their editing histories are free and open source (FOSS) under the MIT license. All software packages and external libraries used were also ensured to be FOSS before use.
\subsection{Environmental Considerations}
One possible source for environmental impact, when it comes to adversarial defenses, is reducing energy consumption. Outside of academia, the most likely victim for an adversarial attack is AI web services hosted in data centers. These data centers are always online, so by reducing the energy use, the amount of fossil fuels consumed to power the data center can be reduced. For services dealing with only high-contrast, greyscale datasets, we recommend looking into a filter defense to avoid wasting energy training a denoising autoencoder.
While admittedly, slightly reducing energy consumption will not make the difference in the long run, doing anything to reduce power consumption helps.
\subsection{Economic and Financial Considerations}
As noted several times previously, online services have new cybersecurity risks when it comes to machine learning. Dealing with adversarial examples may result in financial damages whether in the form of lawsuits or lost sales due to sabotage.
One benefit of filtering over a denoising autoencoder is a deterministic output. Since filters behave like any other algorithmic method, the output of any input is easily determined. This way, auxiliary algorithms such as checksums or cryptographic signing can be used to verify inputs to the model. The determinism of filtering opens the door to enhanced cybersecurity integrations.
Unfortunately, this determinism may also put the service at risk from reverse engineered attacks, since it is much easier to reverse engineer an algorithm than an autoencoder's parameters. Still, the potential cybersecurity advantages that come with an algorithmic approach drove much of the curiosity behind this project.
\subsection{Generative AI Disclaimer}
All code, results data, writing, figures, equations, typos, and errors, unless a source is cited, are the words and work of the authors listed on the title page. ChatGPT 3.5 was used to assist with the wording of the title of this paper, but no other generative AI was used in this project.
\section{Key Takeaways}
This project started with very little knowledge about machine learning, let alone adversarial machine learning. We had some prior experience working with image processing and filtering in Python, which partly inspired our approach. For the most part, however, all knowledge presented at the end of the semester was learned throughout the semester. Everything from builing custom models to implementing, training, and validating machine learning models, to learning how to attack a model, to even learning how to use Matplotlib's 3D functionionality.
The primary source of information was library documentation, after that, it was teammates with prior experience in adversarial machine learning. Other questions not answerable by those sources were answered by reading prior papers published on filters and adversarial machine learning.
\section{Future Work}
While significant progress was made in developing the proposed pipeline, we did not meet all of our requirements, and therefore are not yet in a position to publish this work. To meet these requirements, we must still test at least one more attack, likely Carlini and Wagner \cite{carlini2017evaluating}.
Additionally, we find it important to standardize the strength parameter with a more robust, likely SNR-based definition. In doing so, calling the parameter "strength" would make more intuitive sense, and it would provide a fairer comparison of the filters.
Third, we would like to test the hypothesis that filtering is more energy efficient than a denoising autoencoder. If it is more energy efficient, then the proposed pipeline may be appealing to data centers whose primary cost-cutting measure is to reduce power consumption.
Finally, we plan to implement a few more alternative filters. Some of which will be quite easy to implement (simply a call to a library function), while others will require a custom implementation.
\section{Acknowledgements}
In any academic effort, we stand on the shoulders of giants. This project is no different and would not have been possible without help of several key resources. Special thanks to the PyTorch examples team for providing the baseline implementation of the LeNet classifier used on MNIST and the implementation of the FGSM attack with varying strengths. This project also heavily used the NumPy, Matplotlib, PyTorch libraries. Filters not implemented by the authors used the cv2 distribution of OpenCV for Python, and the Kuwahara filters were implemented using the pykuwahara library. Additionally, we would not have found VGG16 or DLA without the help from kuangliu on GitHub, who supplied the source code and performances of a set of models on CIFAR-10. To fix the rendering of Matplotlib's 3D bar chart, code from codeMonkey, marisano, and CYang on Stack Overflow was used. Finally, we would like to thank Dr. Robi Polikar and Chaz Allegra for their mentorship and unending support throughout the course of this project. Without their help and dedication, we would not have made nearly as much progress this semester.
\pagebreak
\printbibliography
\end{document}

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\contentsline {section}{\numberline {1}Introduction}{2}{}%
\contentsline {section}{\numberline {2}Approach and Experimental Timeline}{2}{}%
\contentsline {section}{\numberline {3}Constraints}{3}{}%
\contentsline {section}{\numberline {4}Alternative Filters}{3}{}%
\contentsline {section}{\numberline {5}Results and Evaluation}{4}{}%
\contentsline {section}{\numberline {6}Conclusions}{5}{}%
\contentsline {section}{\numberline {7}Ethical Considerations}{5}{}%
\contentsline {subsection}{\numberline {7.1}Heath and Safety Considerations}{5}{}%
\contentsline {subsection}{\numberline {7.2}Social and Cultural Considerations}{6}{}%
\contentsline {subsection}{\numberline {7.3}Environmental Considerations}{6}{}%
\contentsline {subsection}{\numberline {7.4}Economic and Financial Considerations}{6}{}%
\contentsline {subsection}{\numberline {7.5}Generative AI Disclaimer}{6}{}%
\contentsline {section}{\numberline {8}Key Takeaways}{6}{}%
\contentsline {section}{\numberline {9}Future Work}{7}{}%
\contentsline {section}{\numberline {10}Acknowledgements}{7}{}%

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@ -75,3 +75,12 @@
keywords={Polynomials;Boosting;Laboratories;Computer science;Upper bound;Boolean functions;Filtering},
doi={10.1109/SFCS.1989.63451}
}
@article{kuwahara1976processing,
title={Processing of RI-angiocardiographic images},
author={Kuwahara, Michiyoshi and Hachimura, Kozaburo and Eiho, Shigeru and Kinoshita, Masato},
journal={Digital processing of biomedical images},
pages={187--202},
year={1976},
publisher={Springer}
}

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\documentclass{article}
\usepackage{graphicx}
\usepackage{float}
\usepackage[margin=1in]{geometry}
\usepackage[backend=biber]{biblatex}
\addbibresource{references.bib}
\title{Enhancing Image Classifiers with Denoising Filters}
\author{Aidan Sharpe}
\date{}
\begin{document}
\begin{titlepage}
\maketitle
\end{titlepage}
\newpage
\tableofcontents
\pagebreak
\begin{abstract}
\end{abstract}
\section{Introduction}
All neural networks are prone to adversarial attacks \cite{carlini2017evaluating} \cite{szegedy2014intriguing}.
\printbibliography
\end{document}

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\contentsline {section}{\numberline {1}Introduction}{2}{}%

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@ -13,7 +13,7 @@ import torch.nn.functional as F
#import dla
import vgg
import defense_filters
EPOCHS = 40
@ -64,6 +64,11 @@ def train(model, trainloader, device, optimizer, criterion, epoch):
# zero the parameter gradients
optimizer.zero_grad()
images = defense_filters.pttensor_to_images(data)
for image in images:
plt.imshow(image)
plt.show()
# forward + backward + optimize
output = model(data)
loss = criterion(output, target)

28
src/defense_filters.py
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@ -9,14 +9,18 @@ import torch
def pttensor_to_images(data):
images = None
try:
images = data.numpy().transpose(0,2,3,1)
#images = data.numpy().transpose(0,2,3,1)
images = data.permute(0,2,3,1).numpy()
except TypeError:
try:
images = data.cpu().numpy().transpose(0,2,3,1)
images = data.cpu().permute(0,2,3,1).numpy()
#images = data.cpu().numpy().transpose(0,2,3,1)
except RuntimeError:
images = data.cpu().detach().numpy().transpose(0,2,3,1)
images = data.cpu().detach().permute(0,2,3,1).numpy()
#images = data.cpu().detach().numpy().transpose(0,2,3,1)
except RuntimeError:
images = data.detach().numpy().transpose(0,2,3,1)
images = data.detach().permute(0,2,3,1).numpy()
#images = data.detach().numpy().transpose(0,2,3,1)
return images
@ -29,7 +33,7 @@ def gaussian_kuwahara(data, dimensions, radius=5):
filtered_images[i] = kuwahara(images[i], method='gaussian', radius=radius, image_2d=images[i])
if i == 0 and radius == 5:
plt.title("Gaussian Kuwahara", fontsize=40)
plt.imshow(filtered_images[i], cmap='gray')
plt.imshow(filtered_images[i])
plt.show()
filtered_images = filtered_images.transpose(0,3,1,2)
@ -44,7 +48,7 @@ def mean_kuwahara(data, dimensions, radius=5):
filtered_images[i] = kuwahara(images[i], method='mean', radius=radius, image_2d=images[i])
if i == 0 and radius == 5:
plt.title("Mean Kuwahara", fontsize=40)
plt.imshow(filtered_images[i], cmap='gray')
plt.imshow(filtered_images[i])
plt.show()
filtered_images = filtered_images.transpose(0,3,1,2)
@ -63,7 +67,7 @@ def random_noise(data, dimensions, intensity=0.001):
filtered_images[i] = cv2.addWeighted(images[i], 1.0, noise, intensity, 0.0).reshape(filtered_images[i].shape)
if i == 0 and intensity == 0.0015:
plt.title("Random Noise", fontsize=40)
plt.imshow(filtered_images[i], cmap='gray')
plt.imshow(filtered_images[i])
plt.show()
@ -79,10 +83,10 @@ def gaussian_blur(data, dimensions, ksize=(5,5)):
filtered_images[i] = cv2.GaussianBlur(images[i], ksize=ksize, sigmaX=0).reshape(filtered_images[i].shape)
if i == 0 and ksize[0] == 5:
plt.title("Unfiltered", fontsize=40)
plt.imshow(images[i], cmap='gray')
plt.imshow(images[i])
plt.show()
plt.title("Gaussian Blur", fontsize=40)
plt.imshow(filtered_images[i], cmap='gray')
plt.imshow(filtered_images[i])
plt.show()
filtered_images = filtered_images.transpose(0,3,1,2)
@ -97,7 +101,7 @@ def bilateral_filter(data, dimensions, d=5, sigma=50):
filtered_images[i] = cv2.bilateralFilter(images[i], d, sigma, sigma).reshape(filtered_images[i].shape)
if i == 0 and d == 5:
plt.title("Bilateral Filter", fontsize=40)
plt.imshow(filtered_images[i], cmap='gray')
plt.imshow(filtered_images[i])
plt.show()
filtered_images = filtered_images.transpose(0,3,1,2)
@ -127,7 +131,7 @@ def threshold_filter(data, dimensions, threshold=0.5):
if i == 0 and threshold == 0.5:
plt.title("Threshold Filter", fontsize=40)
plt.imshow(filtered_images[i], cmap='gray')
plt.imshow(filtered_images[i])
plt.show()
filtered_images = filtered_images.transpose(0,3,1,2)
@ -144,7 +148,7 @@ def bit_depth(data, dimensions, bits=16):
if i == 0 and bits == 4:
plt.title("Bit-Depth", fontsize=40)
plt.imshow(filtered_images[i], cmap='gray')
plt.imshow(filtered_images[i])
plt.show()
filtered_images = filtered_images.transpose(0,3,1,2)

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@ -0,0 +1 @@
Subproject commit 44993391ac9444b9941596d7fec6627fe6673910

41
src/test_defenses.py
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@ -20,40 +20,40 @@ import defense_filters
ATTACK = "FGSM"
DATASET = "MNIST"
DATASET = "CIFAR-10"
RES_X = 28
RES_Y = 28
CHANNELS = 1
RES_X = 32
RES_Y = 32
CHANNELS = 3
MIN_EPSILON = 0.2
MIN_EPSILON = 0.05
MAX_EPSILON = 0.3
EPSILON_STEP = 0.025
TESTED_STRENGTH_COUNT = 5
epsilons = np.arange(MIN_EPSILON, MAX_EPSILON+EPSILON_STEP, EPSILON_STEP)
pretrained_model = "mnist_cnn_unfiltered.pt"
pretrained_model = "cifar_vgg.pth"
use_cuda=False
torch.manual_seed(69)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('data/', train=False, download=True, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,)),
])),
batch_size=1, shuffle=False)
#test_loader = torch.utils.data.DataLoader(
# datasets.MNIST('data/', train=False, download=True, transform=transforms.Compose([
# transforms.ToTensor(),
# transforms.Normalize((0.1307,), (0.3081,)),
# ])),
# batch_size=1, shuffle=False)
#transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
#batch_size = 1
#testset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
#test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=True, num_workers=2)
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
batch_size = 1
testset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=2)
print("CUDA Available: ", torch.cuda.is_available())
device = torch.device("cuda" if use_cuda and torch.cuda.is_available() else "cpu")
model = mnist.Net().to(device)
model = vgg.VGG("VGG16").to(device)
model.load_state_dict(torch.load(pretrained_model, map_location=device))
@ -128,8 +128,15 @@ def test(model, device, test_loader, epsilon, filter):
# Restore the data to its original scale
data_denorm = denorm(data)
images = defense_filters.pttensor_to_images(data)
plt.imshow(images[0])
plt.show()
# Apply the FGSM attack
perturbed_data = fgsm_attack(data_denorm, epsilon, data_grad)
# Reapply normalization
perturbed_data_normalized = transforms.Normalize((0.1307,), (0.3081,))(perturbed_data)