Adversarial Attacks: Unterschied zwischen den Versionen
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** Jupyter Notebook: https://github.com/oscarknagg/adversarial/blob/master/notebooks/Creating_And_Defending_From_Adversarial_Examples.ipynb | ** Jupyter Notebook: https://github.com/oscarknagg/adversarial/blob/master/notebooks/Creating_And_Defending_From_Adversarial_Examples.ipynb | ||
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=WHITE/BLACK BOX ATTACKS= | =WHITE/BLACK BOX ATTACKS= | ||
==on voice (ASR)== | ==on voice (ASR)== | ||
Version vom 24. April 2019, 15:34 Uhr
- 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/
Inhaltsverzeichnis
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
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:
WHITE/BLACK BOX ATTACKS
on voice (ASR)
Psychoacoustic Hiding (Attacking Speech Recognition)
BLACK BOX ATTACKS
- https://medium.com/@ml.at.berkeley/tricking-neural-networks-create-your-own-adversarial-examples-a61eb7620fd8
- Jupyter Notebook: https://github.com/dangeng/Simple_Adversarial_Examples
on computer vision
propose zeroth order optimization (ZOO)
- attacks to directly estimate the gradients of the targeted DNN
Black-Box Attacks using Adversarial Samples
- a technique that uses the victim model as an oracle to label a synthetic training set for the substitute, so the attacker need not even collect a training set to mount the attack
new Tesla Hack
- 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
on voice (ASR)
- https://www.theregister.co.uk/2016/07/11/siri_hacking_phones/
- https://www.fastcompany.com/90240975/alexa-can-be-hacked-by-chirping-birds
BLACK BOX / WHITE BOX ATTACKS
on voice (ASR)
Psychoacoustic Hiding (Attacking Speech Recognition)
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/
Anti Surveillance
http://dismagazine.com/dystopia/evolved-lifestyles/8115/anti-surveillance-how-to-hide-from-machines/