Adversarial Examples in Machine Learning
19 Oct 2018 11:06Yet another inadequate place-holder. (TODO: Dredge up my reading-group notes from 2015.)
This phenomenon is, to my mind, the most interesting thing to have come out of the recent revival of multi-layer connectionist models, a.k.a. "deep learning".
- Recommended:
- Sébastien Bubeck, Eric Price, Ilya Razenshteyn, "Adversarial examples from computational constraints", arxiv:1805.10204
- Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt, "Visual Causal Feature Learning", arxiv:1412.2309
- Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy, "Explaining and Harnessing Adversarial Examples", arxiv:1412.6572
- Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard, "Universal adversarial perturbations", arxiv:1610.08401
- Anh Nguyen, Jason Yosinski, Jeff Clune, "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images", arxiv:1412.1897
- Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus, "Intriguing properties of neural networks", arxiv:1312.6199
- To read:
- Anish Athalye, Logan Engstrom, Andrew Ilyas, Kevin Kwok, "Synthesizing Robust Adversarial Examples", arxiv:1707.07397
- Tom B. Brown, Dandelion Mané, Aurko Roy, MartÃn Abadi, Justin Gilmer, "Adversarial Patch", arxiv:1712.09665
- Gamaleldin F. Elsayed, Ian Goodfellow, Jascha Sohl-Dickstein, "Adversarial Reprogramming of Neural Networks", arxiv:1806.11146
- Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian Goodfellow, Jascha Sohl-Dickstein, "Adversarial Examples that Fool both Computer Vision and Time-Limited Humans", arxiv:1802.08195
- Samuel G. Finlayson, Hyung Won Chung, Isaac S. Kohane, Andrew L. Beam, "Adversarial Attacks Against Medical Deep Learning Systems", arxiv:1804.05296
- Justin Gilmer, Ryan P. Adams, Ian Goodfellow, David Andersen, George E. Dahl, "Motivating the Rules of the Game for Adversarial Example Research", arxiv:1807.06732 [Not sure how much this matters to me, since I'm not interested in these as security holes so much as windows on to what the networks are doing]
- Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian Goodfellow, "Adversarial Spheres", arxiv:1801.02774
- Jason Jo, Yoshua Bengio, "Measuring the tendency of CNNs to Learn Surface Statistical Regularities", arxiv:1711.11561
