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May 14, 2019 at 00:29
Generating photos of fictional people using generative adversarial networks: https://thispersondoesnotexist.com/

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Oct 30, 2024 at 00:10
It's incredibly hard to explain adversarial problems to users. I see gamers sincerely asking "why doesn't the publisher just fix the cheater problem?". This is exacerbated by the fact that sharing too many details of anticheat can make the problem worse.
Jan 20, 2020 at 14:33
Invited talk: Safety Verification for Deep Neural Networks: https://popl20.sigplan.org/details/VMCAI-2020-papers/22/Safety-and-Robustness-for-Deep-Learning-with-Provable-Guarantees How do we verify that a DNN is robust to adversarial attacks? How do we quantify safety? This approach looks at image features (Sift) and verifies all perturbations within a region.
Safety and Robustness for Deep Learning with Provable Guarantees (VMCAI 2020 - - 21st International Conference on Verification, Model Checking, and Abstract Interpretation) - POPL 2020
Welcome to the website of the 21st International Conference on Verification, Model Checking, and Abstract Interpretation (VMCAI 2020). VMCAI provides a forum for researchers from the communities of Verification, Model Checking, and Abstract Interpretation, facilitating interaction, cross-fertilization, and advancement of hybrid methods that combine these and related areas. VMCAI 2020 will be the 21st edition in the series. Proceedings https://link.springer.com/book/10.1007/978-3-030-39322-9 Invited Speakers We are pleased to announce the the invited speakers for VMCAI 2020 are: Rajeev ...
Aug 17, 2019 at 04:31
It's fascinating to see the adversarial relationship between the developers of Incognito Mode and those trying to detect it. JS is a very rich environment with a ton of options. https://www.bleepingcomputer.com/news/google/google-chrome-incognito-mode-can-still-be-detected-by-these-methods/