Adversarial Machine Learning explained! | With examples.
Hint: Stay until the end of the video for an adversarial attack compilation!
🤔 Ever wondered what adversarial attacks are? What FGSM, the Fast Gradient Sign method, is? What the difference between Adversarial Machine Learning and Generative Adversarial Networks is? If yes, you have found the right video for you!
Outline:
* 00:00 Difference between GANs and Adversarial ML
* 01:04 Noise Attack
* 01:52 Fast Gradient Sign Method (FGSM)
* 03:53 Targeted vs Untargeted
* 04:53 White box vs Black box
* 05:38 Adversarial examples
* 06:32 Defenses against adversarial attacks
* 08:46 Aversarial examples COMPILATION
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🔥 Optionally, pay us a coffee to boost our Coffee Bean production! ☕
Patreon: https://www.patreon.com/AICoffeeBreak
Ko-fi: https://ko-fi.com/aicoffeebreak
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For all the juicy details about the topic, check out this video @Stanford University School of Engineering : https://www.youtube.com/watch?v=CIfsB_EYsVI
📄 Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. "Explaining and harnessing adversarial examples." arXiv preprint arXiv:1412.6572 (2014). https://arxiv.org/pdf/1412.6572.pdf
📄 Thys, Simen, Wiebe Van Ranst, and Toon Goedemé. "Fooling automated surveillance cameras: adversarial patches to attack person detection." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 0-0. 2019. https://arxiv.org/pdf/1904.08653.pdf
📄 Belinkov, Yonatan, and Yonatan Bisk. "Synthetic and natural noise both break neural machine translation." arXiv preprint arXiv:1711.02173 (2017). https://arxiv.org/pdf/1711.02173.pdf
🔗 Links:
YouTube: https://www.youtube.com/channel/UCobqgqE4i5Kf7wrxRxhToQA/
Twitter: https://twitter.com/AICoffeeBreak
Reddit: https://www.reddit.com/r/AICoffeeBreak/
#AICoffeeBreak #MsCoffeeBean #AdversarialML #MachineLearning #AI #research
Video contains emojis designed by OpenMoji – the open-source emoji and icon project. License: CC BY-SA 4.0
Видео Adversarial Machine Learning explained! | With examples. канала AI Coffee Break with Letitia
🤔 Ever wondered what adversarial attacks are? What FGSM, the Fast Gradient Sign method, is? What the difference between Adversarial Machine Learning and Generative Adversarial Networks is? If yes, you have found the right video for you!
Outline:
* 00:00 Difference between GANs and Adversarial ML
* 01:04 Noise Attack
* 01:52 Fast Gradient Sign Method (FGSM)
* 03:53 Targeted vs Untargeted
* 04:53 White box vs Black box
* 05:38 Adversarial examples
* 06:32 Defenses against adversarial attacks
* 08:46 Aversarial examples COMPILATION
▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀
🔥 Optionally, pay us a coffee to boost our Coffee Bean production! ☕
Patreon: https://www.patreon.com/AICoffeeBreak
Ko-fi: https://ko-fi.com/aicoffeebreak
▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀
For all the juicy details about the topic, check out this video @Stanford University School of Engineering : https://www.youtube.com/watch?v=CIfsB_EYsVI
📄 Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. "Explaining and harnessing adversarial examples." arXiv preprint arXiv:1412.6572 (2014). https://arxiv.org/pdf/1412.6572.pdf
📄 Thys, Simen, Wiebe Van Ranst, and Toon Goedemé. "Fooling automated surveillance cameras: adversarial patches to attack person detection." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 0-0. 2019. https://arxiv.org/pdf/1904.08653.pdf
📄 Belinkov, Yonatan, and Yonatan Bisk. "Synthetic and natural noise both break neural machine translation." arXiv preprint arXiv:1711.02173 (2017). https://arxiv.org/pdf/1711.02173.pdf
🔗 Links:
YouTube: https://www.youtube.com/channel/UCobqgqE4i5Kf7wrxRxhToQA/
Twitter: https://twitter.com/AICoffeeBreak
Reddit: https://www.reddit.com/r/AICoffeeBreak/
#AICoffeeBreak #MsCoffeeBean #AdversarialML #MachineLearning #AI #research
Video contains emojis designed by OpenMoji – the open-source emoji and icon project. License: CC BY-SA 4.0
Видео Adversarial Machine Learning explained! | With examples. канала AI Coffee Break with Letitia
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24 июля 2020 г. 18:00:18
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