Not with a Bug, But with a Sticker: Attacks on Machine Learning Systems and What to Do about Them

A robust and engaging account of the single greatest threat faced by AI and ML systems

In Not With A Bug, But With A Sticker: Attacks on Machine Learning Systems and What To Do About Them, a team of distinguished adversarial machine learning researchers deliver a riveting account of the most significant risk to currently deployed artificial intelligence systems: cybersecurity threats. The authors take you on a sweeping tour - from inside secretive government organizations to academic workshops at ski chalets to Google's cafeteria - recounting how major AI systems remain vulnerable to the exploits of bad actors of all stripes.

Based on hundreds of interviews of academic researchers, policy makers, business leaders and national security experts, the authors compile the complex science of attacking AI systems with color and flourish and provide a front row seat to those who championed this change. Grounded in real world examples of previous attacks, you will learn how adversaries can upend the reliability of otherwise robust AI systems with straightforward exploits.

The steeplechase to solve this problem has already begun: Nations and organizations are aware that securing AI systems brings forth an indomitable advantage: the prize is not just to keep AI systems safe but also the ability to disrupt the competition's AI systems.

An essential and eye-opening resource for machine learning and software engineers, policy makers and business leaders involved with artificial intelligence, and academics studying topics including cybersecurity and computer science, Not With A Bug, But With A Sticker is a warning--albeit an entertaining and engaging one--we should all heed.

How we secure our AI systems will define the next decade. The stakes have never been higher, and public attention and debate on the issue has never been scarcer.

The authors are donating the proceeds from this book to two charities: Black in AI and Bountiful Children's Foundation.

「不是蟲子，而是貼紙：對機器學習系統的攻擊及應對方法」是一本關於人工智慧（AI）和機器學習（ML）系統所面臨的最大威脅的堅實而引人入勝的描述。在這本書中，一群傑出的對抗性機器學習研究人員提供了一個生動的敘述，闡述了當前部署的人工智慧系統所面臨的最重要的風險：網絡安全威脅。作者們帶領讀者從神秘的政府組織內部，到滑雪小屋的學術研討會，再到谷歌的餐廳，回顧了各種不良行為者如何利用各種手段來攻擊主要的人工智慧系統。

本書基於對學術研究人員、政策制定者、商業領袖和國家安全專家的數百次訪談，以生動的方式整理了攻擊人工智慧系統的複雜科學，並提供了一個近距離觀察這一變革的機會。通過真實世界中以往攻擊的實例，您將了解對手如何通過簡單的攻擊手法破壞原本堅固的人工智慧系統的可靠性。

解決這個問題的競賽已經開始：各國和組織都意識到保護人工智慧系統帶來了無法抗拒的優勢：獎勵不僅是保護AI系統的安全，還包括破壞競爭對手的AI系統的能力。

對於從事機器學習和軟體工程的工程師、政策制定者和商業領袖，以及研究網絡安全和計算機科學等主題的學者來說，這本書是一個必不可少且引人入勝的資源。《不是蟲子，而是貼紙》是一個警示，儘管是一個有趣且引人入勝的警示，我們都應該注意。

我們如何保護我們的人工智慧系統將決定未來十年的走向。賭注從未如此高，而公眾對這個問題的關注和辯論從未如此稀缺。

作者將本書的收益捐贈給兩個慈善機構：Black in AI和Bountiful Children's Foundation。