【AI安全周刊】2022年5月第三期

5月第三周

  1. Machine learning has a backdoor problem https://bdtechtalks.com/2022/05/23/machine-learning-undetectable-backdoors/
  2. (De)ToxiGen: Leveraging large language models to build more robust hate speech detection tools https://www.microsoft.com/en-us/research/blog/detoxigen-leveraging-large-language-models-to-build-more-robust-hate-speech-detection-tools/?OCID=msr_blog_DeToxiGen_TW
  3. Introducing two new datasets to help measure fairness and mitigate AI bias https://ai.facebook.com/blog/measure-fairness-and-mitigate-ai-bias/
  4. Learning Machine Learning Part 3: Attacking Black Box Models https://posts.specterops.io/learning-machine-learning-part-3-attacking-black-box-models-3efffc256909

往期回顾:

2021年10月到12月

1.【论文】关于图神经网络 (Graph Neural Network) 生成的图嵌入 (Graph Embedding) 中的 隐私泄漏的文章Inference Attacks Against Graph Neural Networks

目前该工作已被 Usenix Security 2022 录用 本文着重分析了图嵌入共享过程中可能存在的隐私泄露问题 作者提出了三种攻击场景,即图属性推断攻击、子图推断攻击,以及图重构攻击,并系统地评估了这三种攻击的有效性 最后,作者提出了基于图嵌入扰动的方法来降低这些攻击带来的风险

https://arxiv.org/pdf/2110.02631.pdf

2.AAAI’22|基于验证外源特征的模型偷盗防御 https://zhuanlan.zhihu.com/p/427111937

3.SeqAttack: Token分类模型对抗攻击框架 https://github.com/WalterSimoncini/SeqAttack

4.腾讯朱雀实验室推出代码防护技术Deep Puzzling,让代码更难被猜透 https://www.jiqizhixin.com/articles/2021-11-29

5.机器学习DGA域名检测 https://xz.aliyun.com/t/10522

  1. 没有银弹-AI安全领域的安全与隐私 https://www.anquanke.com/post/id/258704
  2. AI生成代码对网络安全的影响 https://www.cfr.org/blog/ai-code-generation-and-its-implications-cybersecurity
  3. AI赋能windows恶意软件检测 https://www.anquanke.com/post/id/256969

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