On Your Ocean's 11 Team, I'm the AI Guy (technically Girl)

Presented at DEF CON 32 (2024), Aug. 9, 2024, noon (45 minutes).

One of the best parts of DEF CON is the glitz and glam of Vegas, the gambling capital of the world. Many have explored hacking casinos (on and off stage). Unfortunately, it’s just not like it is portrayed in the Oceans franchise.. in real life there’s much less action, no George Clooney, and it’s a lot harder to pull off a successful heist. Fortunately I’m not your typical hacker, I’m an AI hacker. I use adversarial machine learning techniques to disrupt, deceive and disclose information from Artificial Intelligence systems. I chose my target carefully: Canberra Casino. It’s the best casino in my city.. It’s also the only casino but that’s not the point. The casino industry is at an interesting inflection point. Many large casinos have already adopted AI for surveillance and gameplay monitoring, smaller casinos are starting to make the transition, and there’s only a couple of companies in the world that provide this software. It’s ripe for exploitation. In this talk I’m going to show you how I bypassed Casino Canberra's AI systems - facial recognition, surveillance systems and gameplay monitoring. AI Security is the new cyber security threat, and attacks on AI systems could have broad implications including misdiagnoses in medical imaging, navigation errors in autonomous vehicles.. and successful casino heists. 1. Standing Committee of the One Hundred Year Study of Artificial Intelligence. Gathering Strength,Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report | One Hundred Year Study on Artificial Intelligence (AI100). Technical report, September 2021. 2. Eva A. M. van Dis, Johan Bollen, Willem Zuidema, Robert van Rooij, and Claudi L. Bockting. ChatGPT: five priorities for research. Nature, 614(7947):224–226, February 2023. Bandiera abtest: a Cg type: Comment Number: 7947 Publisher: Nature Publishing Group Subject term: Com-puter science, Research management, Publishing, Machine learning. 3. Mingfu Xue, Chengxiang Yuan, Heyi Wu, Yushu Zhang, and Weiqiang Liu. Machine Learn-ing Security: Threats, Countermeasures, and Evaluations. IEEE Access, 8:74720–74742, 2020.Conference Name: IEEE Access. 4. NSCAI. The National Security Commission on Artificial Intelligence. 5. Elisa Bertino, Murat Kantarcioglu, Cuneyt Gurcan Akcora, Sagar Samtani, Sudip Mittal, and Maanak Gupta. AI for Security and Security for AI. In Proceedings of the Eleventh ACM Confer-ence on Data and Application Security and Privacy, CODASPY ’21, pages 333–334, New York, NY, USA, April 2021. Association for Computing Machinery. 5. Battista Biggio and Fabio Roli. Wild patterns: Ten years after the rise of adversarial machine learning. Pattern Recognition, 84:317–331, December 2018. 6. Ian Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and Harnessing Adversarial Examples. In International Conference on Learning Representations, 2015. 7. Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. Intriguing properties of neural networks, February 2014. arXiv:1312.6199 [cs]. 8. Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K. Reiter. Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, CCS ’16, pages 1528–1540, New York, NY, USA, October 2016. Association for Computing Machinery. 9. Tom Brown, Dandelion Mane, Aurko Roy, Martin Abadi, and Justin Gilmer. Adversarial Patch. 2017. 10. US Marines Defeat DARPA Robot by Hiding Under a Cardboard Box | Extremetech. 11. Walter David, Paolo Pappalepore, Alexandra Stefanova, and Brindusa Andreea Sarbu. AI-Powered Lethal Autonomous Weapon Systems in Defence Transformation. Impact and Chal-lenges. In Jan Mazal, Adriano Fagiolini, and Petr Vasik, editors, Modelling and Simulation for Autonomous Systems, Lecture Notes in Computer Science, pages 337–350, Cham, 2020. Springer International Publishing. 12. C Wise and J Plested. Developing Imperceptible Adversarial Patches to Camouflage Military Assets From Computer Vision Enabled Technologies, May 2022. arXiv:2202.08892 cs.. 13. Anish Athalye, Nicholas Carlini, and David Wagner. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples. In Proceedings of the 35th International Conference on Machine Learning, pages 274–283. PMLR, July 2018. ISSN: 2640-3498. 14. Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song. Robust Physical-World Attacks on Deep Learning Visual Classification. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1625–1634, Salt Lake City, UT, USA, June 2018. IEEE. 15. Ram Shankar Siva Kumar, Magnus Nystr ̈om, John Lambert, Andrew Marshall, Mario Goertzel, Andi Comissoneru, Matt Swann, and Sharon Xia. Adversarial Machine Learning-Industry Perspectives. In 2020 IEEE Security and Privacy Workshops (SPW), pages 69–75, May 2020.

Presenters:

  • Harriet Farlow - CEO at Mileva Security Labs
    Harriet Farlow is the CEO of AI Security company Mileva Security Labs, a PhD Candidate in Machine Learning Security, and creative mind behind the YouTube channel HarrietHacks. She missed the boat on computer hacking so now she hacks AI and Machine Learning models instead. Her career has spanned consulting, academia, a start-up and Government, but don’t judge her for that one. She also has a Bachelor in Physics and a Master in Cyber Security. She calls Australia home but has lived in the UK and the US. Her ultimate hack was in founding her own AI Security company but if Skynet takes over she will deny everything and pretend the AI stood for Artificial Insemination, like her Mum thinks it does. (Sorry Mum but I’m not really a Medical Doctor).

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