arXiv:2606.15493v1 Announce Type: new Abstract: Model stealing attacks, where adversaries create high-fidelity surrogate models, are a significant threat to the intellectual property of machine learning services. Conventional wisdom suggests these surrogates could provide adversaries with economic leverage comparable to the original service providers. This paper challenges this assumption by evaluating model stealing attacks beyond mere fidelity to the target model. Because query-based extraction provides only partial supervision of the target's input-output behavior, the surrogate is not uniq
This research emerges as AI services become increasingly commercialized and proprietary, making intellectual property protection a critical concern.
It challenges the fundamental assumption about the economic equivalence of stolen models, forcing a re-evaluation of current security strategies and business models for AI services.
The perceived value and threat level of model stealing attacks are redefined, shifting the focus beyond mere fidelity to the more complex aspects of economic leverage.
- · Original AI service providers
- · Developers of robust model protection
- · AI IP lawyers
- · Model thieves
- · Companies relying on stolen models
- · AI service users with weak IP protection
AI companies will reassess their cybersecurity and IP protection strategies, potentially investing more in robust defenses.
There will be a greater emphasis on understanding the unique economic value of original AI models versus their surrogates.
New legal frameworks and industry standards might emerge to address the unique challenges of AI intellectual property and model stealing.
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Read at arXiv cs.LG