Adversarial Machine Learning explained! | With examples. скачать в хорошем качестве

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! ➡️ AI Coffee Break Merch! 🛍️ https://aicoffeebreak.creator-spring.... 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:   / aicoffeebreak   Ko-fi: https://ko-fi.com/aicoffeebreak ▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀ For all the juicy details about the topic, check out this video ‪@stanfordengineering‬ :    • Lecture 16 | Adversarial Examples and Adve...   📄 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:    / @aicoffeebreak   Twitter:   / aicoffeebreak   Reddit:   / 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