Discover the Thrill of the Football State Cup Israel
Immerse yourself in the excitement of the Football State Cup Israel, where passion and skill collide on the pitch. With daily updates on fresh matches and expert betting predictions, this is your ultimate guide to staying ahead in the game. Whether you're a seasoned football enthusiast or new to the sport, our content is designed to keep you informed and engaged.
The Legacy of the Football State Cup Israel
The Football State Cup Israel is a prestigious tournament that has captured the hearts of fans for decades. Known for its intense matches and unexpected outcomes, it offers a unique platform for clubs across the nation to showcase their talent and compete for glory. The cup's rich history is filled with legendary matches and iconic moments that have defined Israeli football.
Daily Match Updates: Stay Informed
Our platform provides real-time updates on every match in the Football State Cup Israel. From pre-match analysis to live commentary, we ensure you never miss a moment of the action. With comprehensive coverage of each game, you can follow your favorite teams and players as they battle it out on the field.
Expert Betting Predictions: Enhance Your Experience
Betting adds an extra layer of excitement to watching football. Our expert analysts offer daily predictions to help you make informed decisions. Whether you're a seasoned bettor or just starting out, our insights can enhance your experience and potentially increase your winnings.
Understanding the Format: How the Cup Works
The Football State Cup Israel follows a knockout format, where teams compete in single-elimination matches. This format ensures that every game is crucial, with no room for error. From the preliminary rounds to the final showdown, each match brings new challenges and opportunities for teams to prove their mettle.
Top Teams to Watch in This Season's Cup
- Maccabi Tel Aviv: With a storied history and a strong squad, Maccabi Tel Aviv is always a team to watch in any competition.
- Hapoel Be'er Sheva: Known for their resilience and tactical prowess, Hapoel Be'er Sheva are formidable opponents.
- Bnei Yehuda: A team with a passionate fan base, Bnei Yehuda consistently delivers exciting performances.
- Ironi Kiryat Shmona: As one of the top clubs in northern Israel, Ironi Kiryat Shmona brings a unique style of play to the tournament.
In-Depth Player Analysis: Key Performers to Watch
Each season brings new stars to the forefront of Israeli football. Our detailed player analysis highlights key performers who could make a significant impact in this year's cup. From seasoned veterans to rising talents, these players are set to deliver unforgettable moments on the pitch.
The Role of Tactics: How Coaches Shape Matches
Tactics play a crucial role in determining the outcome of matches in the Football State Cup Israel. Our expert breakdowns delve into the strategies employed by different coaches, offering insights into how they adapt their game plans to counter opponents' strengths and exploit weaknesses.
Historical Highlights: Memorable Moments from Past Cups
The Football State Cup Israel has witnessed many unforgettable moments over the years. From dramatic last-minute goals to stunning comebacks, these historical highlights remind us why this tournament holds such a special place in Israeli football culture.
The Economic Impact: How Football Drives Local Economies
Beyond entertainment, football has a significant economic impact on local communities. The Football State Cup Israel attracts fans from all over the country, boosting tourism and generating revenue for businesses near stadiums and popular viewing spots.
Community Engagement: How Clubs Connect with Fans
Football clubs in Israel go beyond just playing matches; they actively engage with their communities. Through various initiatives and outreach programs, clubs build strong connections with fans, fostering a sense of belonging and pride among supporters.
The Future of Israeli Football: Trends and Predictions
The landscape of Israeli football is constantly evolving. Our analysis explores emerging trends and predictions for the future, considering factors such as youth development programs, international collaborations, and technological advancements that could shape the sport's trajectory.
Daily Match Predictions: Expert Insights Every Day
Stay ahead of the game with our daily match predictions. Our team of experts provides detailed analysis and forecasts for each fixture in the Football State Cup Israel. From assessing team form and head-to-head records to considering key player performances, our predictions aim to give you an edge in your betting strategies.
Interactive Features: Engage with Other Fans
Our platform offers interactive features that allow you to engage with other football fans. Participate in discussions, share your opinions on matches, and connect with like-minded enthusiasts who share your passion for Israeli football.
Behind-the-Scenes Access: Meet the Players and Staff
Get an exclusive look behind the scenes with our interviews and profiles of players and staff involved in the Football State Cup Israel. Discover their personal stories, training routines, and insights into what it takes to compete at the highest level.
Social Media Integration: Follow Your Favorite Teams Online
Stay connected with your favorite teams through social media integration on our platform. Follow live updates, engage with official club accounts, and be part of a global community celebrating Israeli football.
Statistical Analysis: Data-Driven Insights into Matches
Data plays an increasingly important role in understanding football dynamics. Our statistical analysis provides data-driven insights into match performances, helping fans appreciate the nuances of strategy and execution that define successful teams.
Cultural Significance: Football as More Than Just a Sport
In Israel, football is more than just a sport; it's a cultural phenomenon that unites people across diverse backgrounds. Our content explores how football reflects societal values and contributes to national identity.
User-Generated Content: Share Your Passion with Others
chapter{Background}
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appendix
chapter{Papers}
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chapter{Code}
The code used throughout this thesis can be found at url{https://github.com/maxeddi/dissertation}.
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<|repo_name|>maxeddi/thesis<|file_sep|>/tex/introduction/intro.tex
This dissertation explores various aspects surrounding adversarial examples for deep neural networks (DNNs), including methods for creating them (both targeted cite{goodfellow2015explaining} and untargeted cite{szegedy2014intriguing}) as well as defense strategies against them.
The problem was first discovered by Szegedy et al. citeyearpar{szegedy2014intriguing} while exploring DNNs used for image classification (a task where DNNs have achieved state-of-the-art results). They noticed that although DNNs perform well on clean images (unmodified images from an image dataset), when given perturbed images (images modified by adding noise) they often fail completely.
Their method used gradient descent cite{jacobs1988increasing} on an image $x$ using $J(theta,x,y)$ where $theta$ are model parameters (weights), $x$ is an input image and $y$ is its correct label (target class). By minimizing $J(theta,x,y)$ they generated an adversarial example $x'$ which would be classified incorrectly by their model.
The main contribution of this dissertation was discovering two new defense strategies against adversarial examples which significantly improve upon existing defenses.
The first strategy improves upon adversarial training cite{xu2017adversarial}, which trains DNNs using adversarial examples rather than clean examples alone cite{xu2017adversarial}. We found that adversarially training DNNs using examples from multiple sources at once significantly improves their performance against adversarial examples created using different attack methods.
The second strategy improves upon input transformations cite{xie2017ensemble}, which apply transformations such as noise addition or JPEG compression before feeding inputs into DNNs cite{xie2017ensemble}. We found that applying multiple transformations before classifying inputs improves performance against adversarial examples more than applying just one transformation.<|repo_name|>maxeddi/thesis<|file_sep|>/tex/paper2/paper.tex
%!TEX root = ../../dissertation.tex
chapter{fulltitle} label{paper2}
noindent
Maximilian Dieterich \
url{[email protected]} \
Technische Universität München \
Department of Informatics \
{ttfamily Group Computational Perception} \
Distinguished Chair Mobile Intelligence \
{ttfamily Prof. Dr. Thomas Brox} \
Advisor: {ttfamily Dr. Ulrich Nehab}\
noindent
today\
noindent
{bf Abstract}\
Deep neural networks have achieved state-of-the-art results on many computer vision tasks such as object detection cite{krizhevsky2012imagenet} or semantic segmentation cite{long2015fully}. However,
it was recently discovered that deep neural networks are highly susceptible to adversarial attacks where small perturbations are added to input images which result in incorrect classifications cite{szegedy2014intriguing}.
To mitigate this issue several defense strategies have been proposed including adversarial training cite{xu2017adversarial} or input transformations cite{xie2017ensemble}. However,
these strategies were only evaluated using one specific attack method.
We propose two new defense strategies against adversarial attacks which significantly improve upon existing defenses.
The first strategy improves upon adversarial training by training deep neural networks using adversarial examples created using multiple attack methods simultaneously.
We show that this improves robustness against all evaluated attack methods compared to training using only one attack method.
The second strategy improves upon input transformations by applying multiple transformations before feeding inputs into deep neural networks.
We show that this improves robustness against all evaluated attack methods compared to applying only one transformation.
Furthermore we demonstrate that both strategies can be combined resulting in further improvements.
noindent
{bf Introduction}\
Deep neural networks have achieved state-of-the-art results on many computer vision tasks such as object detection cite{krizhevsky2012imagenet} or semantic segmentation cite{long2015fully}.
However,
it was recently discovered that deep neural networks are highly susceptible to adversarial attacks where small perturbations are added to input images which result in incorrect classifications cite{szegedy2014intriguing}.
To mitigate this issue several defense strategies have been proposed including adversarial training cite{xu2017adversarial} or input transformations cite{xie2017ensemble}.
However,
these strategies were only evaluated using one specific attack method.
In this paper we propose two new defense strategies against adversarial attacks which significantly improve upon existing defenses.
The first strategy improves upon adversarial training by training deep neural networks using adversarial examples created using multiple attack methods simultaneously.
We show that this improves robustness against all evaluated attack methods compared to training using only one attack method.
The second strategy improves upon input transformations by applying multiple transformations before feeding inputs into deep neural networks.
We show that this improves robustness against all evaluated attack methods compared to applying only one transformation.
Furthermore we demonstrate that both strategies can be combined resulting in further improvements.
noindent
{bf Related Work}\
Adversarial attacks have been explored extensively over recent years due to their potential real-world impact.
Szegedy et al. citeyearpar{szegedy2014intriguing} discovered them while exploring DNNs used for image classification (a task where DNNs have achieved state-of-the-art results).
They noticed that although DNNs perform well on clean images (unmodified images from an image dataset), when given perturbed images (images modified by adding noise) they often fail completely.
Their method used gradient descent cite{jacobs1988increasing} on an image $x$ using $J(theta,x,y)$ where $theta$ are model parameters (weights), $x$ is an input image and $y$ is its correct label (target class).
By minimizing $J(theta,x,y)$ they generated an adversarial example $x'$ which would be classified incorrectly by their model.
Goodfellow et al. proposed several targeted attack methods including FGSM citeyearpar{goodfellow2015explaining}, BIM citeyearpar{goodfellow2015explaining}, JSMA citeyearpar{goodfellow2015explaining}, CWL2 citeyearpar{carlini2016towards}, MIM citeyearpar{xie2017mim}, DeepFool citeyearpar{moosavi-dezfooli2016deepfool}, SPAM citeyearpar{jia2020sparse}, HAMMER-attack citeyearpar{suciu2020hammer} or C&W L-inf attack (CWL-inf) citeyearpar{suciu2020hammer}.
Untargeted attacks include BIM without targeting a specific class (texttt{nBIM}) or DeepFool without targeting any other class (texttt{nDeepFool}) as well as PGD-Linf attack (texttt{nPGD-Linf}) which was proposed later by Madry et al. @onlinecite{nips17_1879}.
All attacks except DeepFool use projected gradient descent.
Adversarial training was proposed by Goodfellow et al. @onlinecite[pg~19]{goodfellow2015explaining} but only explored briefly due to its impracticality at that time.
Xu et al. @onlinecite{xu2017adversarial} revisited it due to advances in computing power making it more feasible.
They found it very effective when applied during standard model training.
They also observed that training models on more diverse datasets improved performance against more diverse attacks.
Input transformations were proposed by Xie et al. @onlinecite{xie2017ensemble}.
They applied transformations such as noise addition or JPEG compression before feeding inputs into DNNs.
They also found it very effective when applied during standard model evaluation.
Both defense strategies were evaluated separately using one specific attack method each (texttt{nPGD-Linf} for adversarial training and FGSM for input transformations).
In this paper we propose two new defense strategies which improve upon existing defenses:
the first strategy improves upon adversarial training by training deep neural networks using adversarial examples created using multiple attack methods simultaneously.
We show that this improves robustness against all evaluated attack methods compared to training using only one attack method.
The second strategy improves upon input transformations by applying multiple transformations before feeding inputs into deep neural networks.
We show that this improves robustness against all evaluated attack methods compared to applying only one transformation.
Furthermore we demonstrate that both strategies can be combined resulting in further improvements.
noindent
{bf Defense Strategies}\
We propose two new defense strategies against adversarial attacks which significantly improve upon existing defenses:
the first strategy improves upon adversarial training by training deep neural networks using adversarial examples created using multiple attack methods simultaneously.
We show that this improves robustness against all evaluated attack methods compared to training using only one attack method.
The second strategy improves upon input transformations by applying multiple transformations before feeding inputs into deep neural networks.
We show that this improves robustness against all evaluated attack methods compared to applying only one transformation.
Furthermore we demonstrate that both strategies can be combined resulting in further improvements.
noindent
{bf Experiments}\
To evaluate our proposed defense strategies we trained ResNet-50 models on ImageNet ILSVRC-12 data set footnote{url{http://image-net.org/}} following standard procedures used during ImageNet ILSVRC-12 competition including data augmentation techniques like random cropping or flipping images horizontally at random.
For each experiment we trained five models with different random seeds initialized randomly from uniform distribution between -1e-2~and~1e-2 per weight parameter following common practices from literature such as Goodfellow et al. @onlinecite[pg~23]{goodfellow2015explaining}.
To evaluate our proposed defense strategies we trained ResNet-50 models on ImageNet ILSVRC-12 data set following standard procedures used during ImageNet ILSVRC-12 competition including data augmentation techniques like random cropping or flipping images horizontally at random.
For each experiment we trained five models with different random seeds initialized randomly from uniform distribution between -1e-2~and~1e-2 per weight parameter following common practices from literature such as Goodfellow et al. @onlinecite[pg~23]{goodfellow2015explaining}.
For each model we trained five times varying learning rate per batch size following common practices from literature such as Goodfellow et al. @onlinecite[pg~22]{goodfellow2015explaining}.
For each learning rate we trained ten times varying learning rate decay per epoch following common practices from literature such as Goodfellow et al. @onlinecite[pg~22]{goodfellow2015explaining}.
Each model was trained until convergence following common practices from literature such as Goodfellow et al. @onlinecite[pg~22]{goodfellow2015explaining}.
For each
