C.heck

Links

einfaches perceptron (schöne skizzen): https://github.com/nature-of-code/NOC-S17-2-Intelligence-Learning/blob/master/week4-neural-networks/perceptron.pdf

adversarial attacks

KNN's sind extrem anfällig für

White Box Attacks

• https://cv-tricks.com/how-to/breaking-deep-learning-with-adversarial-examples-using-tensorflow/
  • Paper »ADVERSARIAL EXAMPLES IN THE PHYSICAL WORLD«: https://arxiv.org/pdf/1607.02533.pdf

Untargeted Adversarial Attacks

Adversarial attacks that just want your model to be confused and predict a wrong class are called Untargeted Adversarial Attacks.

• nicht zielgerichtet

Fast Gradient Sign Method(FGSM)

FGSM is a single step attack, ie.. the perturbation is added in a single step instead of adding it over a loop (Iterative attack).

Basic Iterative Method

Störung, anstatt in einem einzelnen Schritt in mehrere kleinen Schrittgrößen anwenden

Iterative Least-Likely Class Method

ein Bild erstellen, welches in der Vorhersage den niedrigsten Score trägt

• https://medium.com/@ml.at.berkeley/tricking-neural-networks-create-your-own-adversarial-examples-a61eb7620fd8
  • Jupyter Notebook: https://github.com/dangeng/Simple_Adversarial_Examples

Targeted Adversarial Attacks

Attacks which compel the model to predict a (wrong) desired output are called Targeted Adversarial attacks

• zielgerichtet

(Un-)Targeted Adversarial Attacks

kann beides...

Projected Gradient Descent (PGD)

Eine Störung finden die den Verlust eines Modells bei einer bestimmten Eingabe maximiert:

• MNIST-Bsp.: https://towardsdatascience.com/know-your-enemy-7f7c5038bdf3
  • Jupyter Notebook: https://github.com/oscarknagg/adversarial/blob/master/notebooks/Creating_And_Defending_From_Adversarial_Examples.ipynb

BLACK BOX ATTACKS

KNN-Hacks

• Praxis-Beispiele: https://boingboing.net/tag/adversarial-examples
• https://bdtechtalks.com/2018/12/27/deep-learning-adversarial-attacks-ai-malware/
• https://www.dailydot.com/debug/ai-malware/

• https://singularityhub.com/2017/10/10/ai-is-easy-to-fool-why-that-needs-to-change

• https://en.wikipedia.org/wiki/Deep_learning#Cyberthreat

on voice (ASR)

• https://www.the-ambient.com/features/weird-ways-echo-can-be-hacked-how-to-stop-it-231

Psychoacoustic Hiding (Attacking Speech Recognition)

• https://adversarial-attacks.net/
  • Code: https://github.com/rub-ksv/adversarialattacks
  • Paper: https://www.ndss-symposium.org/wp-content/uploads/2019/02/ndss2019_08-2_Schonherr_paper.pdf
  • Präsentationsfolien: https://www.ndss-symposium.org/wp-content/uploads/ndss2019_08-2_Schonherr_slides.pdf

hidden voice commands

• https://www.theregister.co.uk/2016/07/11/siri_hacking_phones/
• https://www.fastcompany.com/90240975/alexa-can-be-hacked-by-chirping-birds

on written text (NLP)

paraphrasing attacks

• https://venturebeat.com/2019/04/01/text-based-ai-models-are-vulnerable-to-paraphrasing-attacks-researchers-find/
• https://bdtechtalks.com/2019/04/02/ai-nlp-paraphrasing-adversarial-attacks/

• https://motherboard.vice.com/en_us/article/9axx5e/ai-can-be-fooled-with-one-misspelled-word

on computer vision

Tesla

• https://spectrum.ieee.org/cars-that-think/transportation/self-driving/three-small-stickers-on-road-can-steer-tesla-autopilot-into-oncoming-lane
• https://boingboing.net/2019/03/31/mote-in-cars-eye.html
  • Paper vom Forschungsteam: https://keenlab.tencent.com/en/whitepapers/Experimental_Security_Research_of_Tesla_Autopilot.pdf

libraries

• https://github.com/bethgelab
• https://github.com/tensorflow/cleverhans

XAI

XAI/NLG

• https://de.m.wikipedia.org/wiki/Explainable_Artificial_Intelligence
• https://netzpolitik.org/2018/enquete-kommission-kuenstliche-intelligenz-sachverstaendige-und-abgeordnete-klaeren-grundbegriffe/
• https://www.ayasdi.com/blog/artificial-intelligence/trust-challenge-explainable-ai-not-enough/
• https://www.bons.ai/blog/explainable-artificial-intelligence-using-model-induction
• https://en.m.wikipedia.org/wiki/Right_to_explanation
• https://bdtechtalks.com/2018/09/25/explainable-interpretable-ai/
• RISE: https://bdtechtalks.com/2018/10/15/kate-saenko-explainable-ai-deep-learning-rise/
• DARPA: https://www.darpa.mil/program/explainable-artificial-intelligence

ethics

• https://www.economist.com/science-and-technology/2018/02/15/computer-programs-recognise-white-men-better-than-black-women
• https://books.google.de/books?id=rLsyDwAAQBAJ&pg=PA95&redir_esc=y#v=onepage&q&f=false
• https://books.google.de/books?id=_H1K3vojDFQC&pg=PA762&redir_esc=y#v=onepage&q&f=false
• https://neil.fraser.name/writing/tank/
• https://www.wired.com/story/why-ai-is-still-waiting-for-its-ethics-transplant/
• AI Now Report: https://medium.com/@AINowInstitute/the-10-top-recommendations-for-the-ai-field-in-2017-b3253624a7
• https://bdtechtalks.com/2018/03/26/racist-sexist-ai-deep-learning-algorithms/

esotheric neural net

• Forscher suchen eigene Programmiersprache: https://t3n.de/news/machine-learning-facebooks-ki-chef-sucht-sprache-1144900/
• esoterische programmiersprachen http://kryptografie.de/kryptografie/chiffre/index-sprachen.htm

KI-generierte Sprache

• Google: https://motherboard.vice.com/de/article/mg7md8/eine-kuenstliche-intelligenz-von-google-hat-gerade-seine-eigene-sprache-erfunden
  • veröffentlichtes Paper: https://arxiv.org/pdf/1611.04558v1.pdf
• FB Bots: https://code.fb.com/ml-applications/deal-or-no-deal-training-ai-bots-to-negotiate/
  • https://www.fastcompany.com/90132632/ai-is-inventing-its-own-perfect-languages-should-we-let-it

NLP / NLG / NLU / NLI

NLP:

• https://de.wikipedia.org/wiki/Computerlinguistik

NLU:

• https://www.informatik-aktuell.de/betrieb/kuenstliche-intelligenz/natural-language-understanding-nlu.html
• https://en.wikipedia.org/wiki/Natural-language_understanding

NLG:

• https://de.wikipedia.org/wiki/Textgenerierung
• http://www.thealit.de/lab/serialitaet/teil/nieberle/nieberle.html

https://github.com/dangeng/Simple_Adversarial_Examples

• https://bdtechtalks.com/2018/02/20/ai-machine-learning-nlg-nlp/
  • https://www.wired.com/2016/03/google-inbox-auto-answers-emails/
  • http://www.wireless-earth.de/oldstuff/oldstuff.html

Speech recognition

https://de.wikipedia.org/wiki/Spracherkennung

=datenbanken

deutsch:

• https://de.wikipedia.org/wiki/GermaNet
  • http://www.sfs.uni-tuebingen.de/GermaNet/

englisch:

• https://wordnet.princeton.edu/

E2E NLG Challenge:

• http://www.macs.hw.ac.uk/InteractionLab/E2E/

chatbots

• https://bdtechtalks.com/2017/08/21/rob-high-ibm-watson-cto-artificial-intelligence-chatbots/
• https://chatbotsmagazine.com/contextual-chat-bots-with-tensorflow-4391749d0077
  • Jupyter Notebook: https://github.com/ugik/notebooks/blob/master/Tensorflow%20chat-bot%20model.ipynb
• Facebook-Messenger-Bot: https://dzone.com/articles/how-i-used-deep-learning-to-train-a-chatbot-to-tal
  • Git-Repo: https://github.com/adeshpande3/Facebook-Messenger-Bot
• https://tutorials.botsfloor.com/how-to-build-your-first-chatbot-c84495d4622d
  • Jupyter Notebooks: https://github.com/suriyadeepan/practical_seq2seq

Toolkits/Librarys

• Natural Language Toolkit: http://www.nltk.org/
• Poetry Generator: https://github.com/schollz/poetry-generator

tryouts:

• https://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/
• https://remicnrd.github.io/Natural-language-generation/
• https://github.com/shashank-bhatt-07/Natural-Language-Generation-using-LSTM-Keras

(KI-generierte) Krypto

• https://motherboard.vice.com/de/article/8q8wkv/google-ki-entwickelt-verschluesselung-die-selbst-google-nicht-versteht
• http://kryptografie.de/kryptografie/index.htm

Reproduktive KI

https://www.sir-apfelot.de/kuenstliche-intelligenz-erschafft-neue-ki-systeme-10436/

last semester

Datei:Neuronales-netz am eigenen-bild.ipynb