AI Product Security: A Primer for Developers
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Abstract (arXiv)
Not too long ago, AI security used to mean the research and practice of how AI can empower cybersecurity, that is, AI for security. Ever since Ian Goodfellow and his team popularized adversarial attacks on machine learning, security for AI became an important concern and also part of AI security. It is imperative to understand the threats to machine learning products and avoid common pitfalls in AI product development. This article is addressed to developers, designers, managers and researchers of AI software products.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 23 references that are in the map. First posted on arXiv 18 Apr 2023.
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References · 9
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Explaining and Harnessing Adversarial Examplescited by 7,287
- 2021Membership Inference Attacks From First Principlescited by 839
- 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 675
- 2017Adversarial Examples: Attacks and Defenses for Deep Learningcited by 350
- 2019High Accuracy and High Fidelity Extraction of Neural Networkscited by 208
- 2018Adversarial Reprogramming of Neural Networkscited by 115
- 2020Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resourcescited by 83
- 2021Privacy and Trust Redefined in Federated Machine Learningcited by 13
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